Species-area relationships and extinction forecasts.

PubMed

Halley, John M; Sgardeli, Vasiliki; Monokrousos, Nikolaos

2013-05-01

The species-area relationship (SAR) predicts that smaller areas contain fewer species. This is the basis of the SAR method that has been used to forecast large numbers of species committed to extinction every year due to deforestation. The method has a number of issues that must be handled with care to avoid error. These include the functional form of the SAR, the choice of equation parameters, the sampling procedure used, extinction debt, and forest regeneration. Concerns about the accuracy of the SAR technique often cite errors not much larger than the natural scatter of the SAR itself. Such errors do not undermine the credibility of forecasts predicting large numbers of extinctions, although they may be a serious obstacle in other SAR applications. Very large errors can arise from misinterpretation of extinction debt, inappropriate functional form, and ignoring forest regeneration. Major challenges remain to understand better the relationship between sampling protocol and the functional form of SARs and the dynamics of relaxation, especially in continental areas, and to widen the testing of extinction forecasts. © 2013 New York Academy of Sciences.

Automated Hypothesis Tests and Standard Errors for Nonstandard Problems with Description of Computer Package: A Draft.

ERIC Educational Resources Information Center

Lord, Frederic M.; Stocking, Martha

A general Computer program is described that will compute asymptotic standard errors and carry out significance tests for an endless variety of (standard and) nonstandard large-sample statistical problems, without requiring the statistician to derive asymptotic standard error formulas. The program assumes that the observations have a multinormal…

An Efficient Silent Data Corruption Detection Method with Error-Feedback Control and Even Sampling for HPC Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di, Sheng; Berrocal, Eduardo; Cappello, Franck

The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in order to improve the detection results. The contributions are twofold: (1) we propose an error feedback control model that can reduce the prediction errors for different linear prediction methods, and (2) we propose a spatial-data-based even-sampling method tomore » minimize the detection overheads (including memory and computation cost). We implement our algorithms in the fault tolerance interface, a fault tolerance library with multiple checkpoint levels, such that users can conveniently protect their HPC applications against both SDC errors and fail-stop errors. We evaluate our approach by using large-scale traces from well-known, large-scale HPC applications, as well as by running those HPC applications on a real cluster environment. Experiments show that our error feedback control model can improve detection sensitivity by 34-189% for bit-flip memory errors injected with the bit positions in the range [20,30], without any degradation on detection accuracy. Furthermore, memory size can be reduced by 33% with our spatial-data even-sampling method, with only a slight and graceful degradation in the detection sensitivity.« less

Error in the Sampling Area of an Optical Disdrometer: Consequences in Computing Rain Variables

PubMed Central

Fraile, R.; Castro, A.; Fernández-Raga, M.; Palencia, C.; Calvo, A. I.

2013-01-01

The aim of this study is to improve the estimation of the characteristic uncertainties of optic disdrometers in an attempt to calculate the efficient sampling area according to the size of the drop and to study how this influences the computation of other parameters, taking into account that the real sampling area is always smaller than the nominal area. For large raindrops (a little over 6 mm), the effective sampling area may be half the area indicated by the manufacturer. The error committed in the sampling area is propagated to all the variables depending on this surface, such as the rain intensity and the reflectivity factor. Both variables tend to underestimate the real value if the sampling area is not corrected. For example, the rainfall intensity errors may be up to 50% for large drops, those slightly larger than 6 mm. The same occurs with reflectivity values, which may be up to twice the reflectivity calculated using the uncorrected constant sampling area. The Z-R relationships appear to have little dependence on the sampling area, because both variables depend on it the same way. These results were obtained by studying one particular rain event that occurred on April 16, 2006. PMID:23844393

Analysis of host response to bacterial infection using error model based gene expression microarray experiments

PubMed Central

Stekel, Dov J.; Sarti, Donatella; Trevino, Victor; Zhang, Lihong; Salmon, Mike; Buckley, Chris D.; Stevens, Mark; Pallen, Mark J.; Penn, Charles; Falciani, Francesco

2005-01-01

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples. PMID:15800204

Previous Estimates of Mitochondrial DNA Mutation Level Variance Did Not Account for Sampling Error: Comparing the mtDNA Genetic Bottleneck in Mice and Humans

PubMed Central

Wonnapinij, Passorn; Chinnery, Patrick F.; Samuels, David C.

2010-01-01

In cases of inherited pathogenic mitochondrial DNA (mtDNA) mutations, a mother and her offspring generally have large and seemingly random differences in the amount of mutated mtDNA that they carry. Comparisons of measured mtDNA mutation level variance values have become an important issue in determining the mechanisms that cause these large random shifts in mutation level. These variance measurements have been made with samples of quite modest size, which should be a source of concern because higher-order statistics, such as variance, are poorly estimated from small sample sizes. We have developed an analysis of the standard error of variance from a sample of size n, and we have defined error bars for variance measurements based on this standard error. We calculate variance error bars for several published sets of measurements of mtDNA mutation level variance and show how the addition of the error bars alters the interpretation of these experimental results. We compare variance measurements from human clinical data and from mouse models and show that the mutation level variance is clearly higher in the human data than it is in the mouse models at both the primary oocyte and offspring stages of inheritance. We discuss how the standard error of variance can be used in the design of experiments measuring mtDNA mutation level variance. Our results show that variance measurements based on fewer than 20 measurements are generally unreliable and ideally more than 50 measurements are required to reliably compare variances with less than a 2-fold difference. PMID:20362273

How allele frequency and study design affect association test statistics with misrepresentation errors.

PubMed

Escott-Price, Valentina; Ghodsi, Mansoureh; Schmidt, Karl Michael

2014-04-01

We evaluate the effect of genotyping errors on the type-I error of a general association test based on genotypes, showing that, in the presence of errors in the case and control samples, the test statistic asymptotically follows a scaled non-central $\\chi ^2$ distribution. We give explicit formulae for the scaling factor and non-centrality parameter for the symmetric allele-based genotyping error model and for additive and recessive disease models. They show how genotyping errors can lead to a significantly higher false-positive rate, growing with sample size, compared with the nominal significance levels. The strength of this effect depends very strongly on the population distribution of the genotype, with a pronounced effect in the case of rare alleles, and a great robustness against error in the case of large minor allele frequency. We also show how these results can be used to correct $p$-values.

Does size matter? Statistical limits of paleomagnetic field reconstruction from small rock specimens

NASA Astrophysics Data System (ADS)

Berndt, Thomas; Muxworthy, Adrian R.; Fabian, Karl

2016-01-01

As samples of ever decreasing sizes are being studied paleomagnetically, care has to be taken that the underlying assumptions of statistical thermodynamics (Maxwell-Boltzmann statistics) are being met. Here we determine how many grains and how large a magnetic moment a sample needs to have to be able to accurately record an ambient field. It is found that for samples with a thermoremanent magnetic moment larger than 10-11Am2 the assumption of a sufficiently large number of grains is usually given. Standard 25 mm diameter paleomagnetic samples usually contain enough magnetic grains such that statistical errors are negligible, but "single silicate crystal" works on, for example, zircon, plagioclase, and olivine crystals are approaching the limits of what is physically possible, leading to statistic errors in both the angular deviation and paleointensity that are comparable to other sources of error. The reliability of nanopaleomagnetic imaging techniques capable of resolving individual grains (used, for example, to study the cloudy zone in meteorites), however, is questionable due to the limited area of the material covered.

Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error.

PubMed

Shipitsin, M; Small, C; Choudhury, S; Giladi, E; Friedlander, S; Nardone, J; Hussain, S; Hurley, A D; Ernst, C; Huang, Y E; Chang, H; Nifong, T P; Rimm, D L; Dunyak, J; Loda, M; Berman, D M; Blume-Jensen, P

2014-09-09

Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a 'high' and a 'low' tumour microarray, respectively. Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error. Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.

Impact of Design Effects in Large-Scale District and State Assessments

ERIC Educational Resources Information Center

Phillips, Gary W.

2015-01-01

This article proposes that sampling design effects have potentially huge unrecognized impacts on the results reported by large-scale district and state assessments in the United States. When design effects are unrecognized and unaccounted for they lead to underestimating the sampling error in item and test statistics. Underestimating the sampling…

Resolution-enhancement and sampling error correction based on molecular absorption line in frequency scanning interferometry

NASA Astrophysics Data System (ADS)

Pan, Hao; Qu, Xinghua; Shi, Chunzhao; Zhang, Fumin; Li, Yating

2018-06-01

The non-uniform interval resampling method has been widely used in frequency modulated continuous wave (FMCW) laser ranging. In the large-bandwidth and long-distance measurements, the range peak is deteriorated due to the fiber dispersion mismatch. In this study, we analyze the frequency-sampling error caused by the mismatch and measure it using the spectroscopy of molecular frequency references line. By using the adjacent points' replacement and spline interpolation technique, the sampling errors could be eliminated. The results demonstrated that proposed method is suitable for resolution-enhancement and high-precision measurement. Moreover, using the proposed method, we achieved the precision of absolute distance less than 45 μm within 8 m.

Secondary analysis of national survey datasets.

PubMed

Boo, Sunjoo; Froelicher, Erika Sivarajan

2013-06-01

This paper describes the methodological issues associated with secondary analysis of large national survey datasets. Issues about survey sampling, data collection, and non-response and missing data in terms of methodological validity and reliability are discussed. Although reanalyzing large national survey datasets is an expedient and cost-efficient way of producing nursing knowledge, successful investigations require a methodological consideration of the intrinsic limitations of secondary survey analysis. Nursing researchers using existing national survey datasets should understand potential sources of error associated with survey sampling, data collection, and non-response and missing data. Although it is impossible to eliminate all potential errors, researchers using existing national survey datasets must be aware of the possible influence of errors on the results of the analyses. © 2012 The Authors. Japan Journal of Nursing Science © 2012 Japan Academy of Nursing Science.

Ptychographic overlap constraint errors and the limits of their numerical recovery using conjugate gradient descent methods.

PubMed

Tripathi, Ashish; McNulty, Ian; Shpyrko, Oleg G

2014-01-27

Ptychographic coherent x-ray diffractive imaging is a form of scanning microscopy that does not require optics to image a sample. A series of scanned coherent diffraction patterns recorded from multiple overlapping illuminated regions on the sample are inverted numerically to retrieve its image. The technique recovers the phase lost by detecting the diffraction patterns by using experimentally known constraints, in this case the measured diffraction intensities and the assumed scan positions on the sample. The spatial resolution of the recovered image of the sample is limited by the angular extent over which the diffraction patterns are recorded and how well these constraints are known. Here, we explore how reconstruction quality degrades with uncertainties in the scan positions. We show experimentally that large errors in the assumed scan positions on the sample can be numerically determined and corrected using conjugate gradient descent methods. We also explore in simulations the limits, based on the signal to noise of the diffraction patterns and amount of overlap between adjacent scan positions, of just how large these errors can be and still be rendered tractable by this method.

Hospital-based transfusion error tracking from 2005 to 2010: identifying the key errors threatening patient transfusion safety.

PubMed

Maskens, Carolyn; Downie, Helen; Wendt, Alison; Lima, Ana; Merkley, Lisa; Lin, Yulia; Callum, Jeannie

2014-01-01

This report provides a comprehensive analysis of transfusion errors occurring at a large teaching hospital and aims to determine key errors that are threatening transfusion safety, despite implementation of safety measures. Errors were prospectively identified from 2005 to 2010. Error data were coded on a secure online database called the Transfusion Error Surveillance System. Errors were defined as any deviation from established standard operating procedures. Errors were identified by clinical and laboratory staff. Denominator data for volume of activity were used to calculate rates. A total of 15,134 errors were reported with a median number of 215 errors per month (range, 85-334). Overall, 9083 (60%) errors occurred on the transfusion service and 6051 (40%) on the clinical services. In total, 23 errors resulted in patient harm: 21 of these errors occurred on the clinical services and two in the transfusion service. Of the 23 harm events, 21 involved inappropriate use of blood. Errors with no harm were 657 times more common than events that caused harm. The most common high-severity clinical errors were sample labeling (37.5%) and inappropriate ordering of blood (28.8%). The most common high-severity error in the transfusion service was sample accepted despite not meeting acceptance criteria (18.3%). The cost of product and component loss due to errors was $593,337. Errors occurred at every point in the transfusion process, with the greatest potential risk of patient harm resulting from inappropriate ordering of blood products and errors in sample labeling. © 2013 American Association of Blood Banks (CME).

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

PubMed Central

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T.; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P.; Rötter, Reimund P.; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations. PMID:27055028

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

PubMed

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P; Rötter, Reimund P; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Error simulation of paired-comparison-based scaling methods

NASA Astrophysics Data System (ADS)

Cui, Chengwu

2000-12-01

Subjective image quality measurement usually resorts to psycho physical scaling. However, it is difficult to evaluate the inherent precision of these scaling methods. Without knowing the potential errors of the measurement, subsequent use of the data can be misleading. In this paper, the errors on scaled values derived form paired comparison based scaling methods are simulated with randomly introduced proportion of choice errors that follow the binomial distribution. Simulation results are given for various combinations of the number of stimuli and the sampling size. The errors are presented in the form of average standard deviation of the scaled values and can be fitted reasonably well with an empirical equation that can be sued for scaling error estimation and measurement design. The simulation proves paired comparison based scaling methods can have large errors on the derived scaled values when the sampling size and the number of stimuli are small. Examples are also given to show the potential errors on actually scaled values of color image prints as measured by the method of paired comparison.

Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline.

PubMed

Dupuy, Lionel X; Wright, Gladys; Thompson, Jacqueline A; Taylor, Anna; Dekeyser, Sebastien; White, Christopher P; Thomas, William T B; Nightingale, Mark; Hammond, John P; Graham, Neil S; Thomas, Catherine L; Broadley, Martin R; White, Philip J

2017-01-01

There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration. Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database. Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.

DNA Barcoding through Quaternary LDPC Codes

PubMed Central

Tapia, Elizabeth; Spetale, Flavio; Krsticevic, Flavia; Angelone, Laura; Bulacio, Pilar

2015-01-01

For many parallel applications of Next-Generation Sequencing (NGS) technologies short barcodes able to accurately multiplex a large number of samples are demanded. To address these competitive requirements, the use of error-correcting codes is advised. Current barcoding systems are mostly built from short random error-correcting codes, a feature that strongly limits their multiplexing accuracy and experimental scalability. To overcome these problems on sequencing systems impaired by mismatch errors, the alternative use of binary BCH and pseudo-quaternary Hamming codes has been proposed. However, these codes either fail to provide a fine-scale with regard to size of barcodes (BCH) or have intrinsic poor error correcting abilities (Hamming). Here, the design of barcodes from shortened binary BCH codes and quaternary Low Density Parity Check (LDPC) codes is introduced. Simulation results show that although accurate barcoding systems of high multiplexing capacity can be obtained with any of these codes, using quaternary LDPC codes may be particularly advantageous due to the lower rates of read losses and undetected sample misidentification errors. Even at mismatch error rates of 10−2 per base, 24-nt LDPC barcodes can be used to multiplex roughly 2000 samples with a sample misidentification error rate in the order of 10−9 at the expense of a rate of read losses just in the order of 10−6. PMID:26492348

DNA Barcoding through Quaternary LDPC Codes.

PubMed

Tapia, Elizabeth; Spetale, Flavio; Krsticevic, Flavia; Angelone, Laura; Bulacio, Pilar

2015-01-01

For many parallel applications of Next-Generation Sequencing (NGS) technologies short barcodes able to accurately multiplex a large number of samples are demanded. To address these competitive requirements, the use of error-correcting codes is advised. Current barcoding systems are mostly built from short random error-correcting codes, a feature that strongly limits their multiplexing accuracy and experimental scalability. To overcome these problems on sequencing systems impaired by mismatch errors, the alternative use of binary BCH and pseudo-quaternary Hamming codes has been proposed. However, these codes either fail to provide a fine-scale with regard to size of barcodes (BCH) or have intrinsic poor error correcting abilities (Hamming). Here, the design of barcodes from shortened binary BCH codes and quaternary Low Density Parity Check (LDPC) codes is introduced. Simulation results show that although accurate barcoding systems of high multiplexing capacity can be obtained with any of these codes, using quaternary LDPC codes may be particularly advantageous due to the lower rates of read losses and undetected sample misidentification errors. Even at mismatch error rates of 10(-2) per base, 24-nt LDPC barcodes can be used to multiplex roughly 2000 samples with a sample misidentification error rate in the order of 10(-9) at the expense of a rate of read losses just in the order of 10(-6).

Amelogenin test: From forensics to quality control in clinical and biochemical genomics.

PubMed

Francès, F; Portolés, O; González, J I; Coltell, O; Verdú, F; Castelló, A; Corella, D

2007-01-01

The increasing number of samples from the biomedical genetic studies and the number of centers participating in the same involves increasing risk of mistakes in the different sample handling stages. We have evaluated the usefulness of the amelogenin test for quality control in sample identification. Amelogenin test (frequently used in forensics) was undertaken on 1224 individuals participating in a biomedical study. Concordance between referred sex in the database and amelogenin test was estimated. Additional sex-error genetic detecting systems were developed. The overall concordance rate was 99.84% (1222/1224). Two samples showed a female amelogenin test outcome, being codified as males in the database. The first, after checking sex-specific biochemical and clinical profile data was found to be due to a codification error in the database. In the second, after checking the database, no apparent error was discovered because a correct male profile was found. False negatives in amelogenin male sex determination were discarded by additional tests, and feminine sex was confirmed. A sample labeling error was revealed after a new DNA extraction. The amelogenin test is a useful quality control tool for detecting sex-identification errors in large genomic studies, and can contribute to increase its validity.

Simulating and assessing boson sampling experiments with phase-space representations

NASA Astrophysics Data System (ADS)

Opanchuk, Bogdan; Rosales-Zárate, Laura; Reid, Margaret D.; Drummond, Peter D.

2018-04-01

The search for new, application-specific quantum computers designed to outperform any classical computer is driven by the ending of Moore's law and the quantum advantages potentially obtainable. Photonic networks are promising examples, with experimental demonstrations and potential for obtaining a quantum computer to solve problems believed classically impossible. This introduces a challenge: how does one design or understand such photonic networks? One must be able to calculate observables using general methods capable of treating arbitrary inputs, dissipation, and noise. We develop complex phase-space software for simulating these photonic networks, and apply this to boson sampling experiments. Our techniques give sampling errors orders of magnitude lower than experimental correlation measurements for the same number of samples. We show that these techniques remove systematic errors in previous algorithms for estimating correlations, with large improvements in errors in some cases. In addition, we obtain a scalable channel-combination strategy for assessment of boson sampling devices.

Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation

NASA Astrophysics Data System (ADS)

Räisänen, Petri; Barker, W. Howard

2004-07-01

The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average broadband radiative fluxes is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. McICA's sampling errors are evaluated here using a global climate model (GCM) dataset and a correlated-k distribution (CKD) radiation scheme. Two approaches to reduce McICA's sampling variance are discussed. The first is to simply restrict all of McICA's samples to cloudy regions. This avoids wasting precious few samples on essentially homogeneous clear skies. Clear-sky fluxes need to be computed separately for this approach, but this is usually done in GCMs for diagnostic purposes anyway. Second, accuracy can be improved by repeated sampling, and averaging those CKD terms with large cloud radiative effects. Although this naturally increases computational costs over the standard CKD model, random errors for fluxes and heating rates are reduced by typically 50% to 60%, for the present radiation code, when the total number of samples is increased by 50%. When both variance reduction techniques are applied simultaneously, globally averaged flux and heating rate random errors are reduced by a factor of #3.

A PRIOR EVALUATION OF TWO-STAGE CLUSTER SAMPLING FOR ACCURACY ASSESSMENT OF LARGE-AREA LAND-COVER MAPS

EPA Science Inventory

Two-stage cluster sampling reduces the cost of collecting accuracy assessment reference data by constraining sample elements to fall within a limited number of geographic domains (clusters). However, because classification error is typically positively spatially correlated, withi...

Chapter 11: Sample Design Cross-Cutting Protocol. The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurnik, Charles W; Khawaja, M. Sami; Rushton, Josh

Evaluating an energy efficiency program requires assessing the total energy and demand saved through all of the energy efficiency measures provided by the program. For large programs, the direct assessment of savings for each participant would be cost-prohibitive. Even if a program is small enough that a full census could be managed, such an undertaking would almost always be an inefficient use of evaluation resources. The bulk of this chapter describes methods for minimizing and quantifying sampling error. Measurement error and regression error are discussed in various contexts in other chapters.

A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification.

PubMed

Jiang, Wenyu; Simon, Richard

2007-12-20

This paper first provides a critical review on some existing methods for estimating the prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimens. Special attention is given to the bootstrap-related methods. When the sample size n is small, we find that all the reviewed methods suffer from either substantial bias or variability. We introduce a repeated leave-one-out bootstrap (RLOOB) method that predicts for each specimen in the sample using bootstrap learning sets of size ln. We then propose an adjusted bootstrap (ABS) method that fits a learning curve to the RLOOB estimates calculated with different bootstrap learning set sizes. The ABS method is robust across the situations we investigate and provides a slightly conservative estimate for the prediction error. Even with small samples, it does not suffer from large upward bias as the leave-one-out bootstrap and the 0.632+ bootstrap, and it does not suffer from large variability as the leave-one-out cross-validation in microarray applications. Copyright (c) 2007 John Wiley & Sons, Ltd.

How Large Should a Statistical Sample Be?

ERIC Educational Resources Information Center

Menil, Violeta C.; Ye, Ruili

2012-01-01

This study serves as a teaching aid for teachers of introductory statistics. The aim of this study was limited to determining various sample sizes when estimating population proportion. Tables on sample sizes were generated using a C[superscript ++] program, which depends on population size, degree of precision or error level, and confidence…

Evaluation of Bayesian Sequential Proportion Estimation Using Analyst Labels

NASA Technical Reports Server (NTRS)

Lennington, R. K.; Abotteen, K. M. (Principal Investigator)

1980-01-01

The author has identified the following significant results. A total of ten Large Area Crop Inventory Experiment Phase 3 blind sites and analyst-interpreter labels were used in a study to compare proportional estimates obtained by the Bayes sequential procedure with estimates obtained from simple random sampling and from Procedure 1. The analyst error rate using the Bayes technique was shown to be no greater than that for the simple random sampling. Also, the segment proportion estimates produced using this technique had smaller bias and mean squared errors than the estimates produced using either simple random sampling or Procedure 1.

Effects of holding time and measurement error on culturing Legionella in environmental water samples.

PubMed

Flanders, W Dana; Kirkland, Kimberly H; Shelton, Brian G

2014-10-01

Outbreaks of Legionnaires' disease require environmental testing of water samples from potentially implicated building water systems to identify the source of exposure. A previous study reports a large impact on Legionella sample results due to shipping and delays in sample processing. Specifically, this same study, without accounting for measurement error, reports more than half of shipped samples tested had Legionella levels that arbitrarily changed up or down by one or more logs, and the authors attribute this result to shipping time. Accordingly, we conducted a study to determine the effects of sample holding/shipping time on Legionella sample results while taking into account measurement error, which has previously not been addressed. We analyzed 159 samples, each split into 16 aliquots, of which one-half (8) were processed promptly after collection. The remaining half (8) were processed the following day to assess impact of holding/shipping time. A total of 2544 samples were analyzed including replicates. After accounting for inherent measurement error, we found that the effect of holding time on observed Legionella counts was small and should have no practical impact on interpretation of results. Holding samples increased the root mean squared error by only about 3-8%. Notably, for only one of 159 samples, did the average of the 8 replicate counts change by 1 log. Thus, our findings do not support the hypothesis of frequent, significant (≥= 1 log10 unit) Legionella colony count changes due to holding. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Maximum type 1 error rate inflation in multiarmed clinical trials with adaptive interim sample size modifications.

PubMed

Graf, Alexandra C; Bauer, Peter; Glimm, Ekkehard; Koenig, Franz

2014-07-01

Sample size modifications in the interim analyses of an adaptive design can inflate the type 1 error rate, if test statistics and critical boundaries are used in the final analysis as if no modification had been made. While this is already true for designs with an overall change of the sample size in a balanced treatment-control comparison, the inflation can be much larger if in addition a modification of allocation ratios is allowed as well. In this paper, we investigate adaptive designs with several treatment arms compared to a single common control group. Regarding modifications, we consider treatment arm selection as well as modifications of overall sample size and allocation ratios. The inflation is quantified for two approaches: a naive procedure that ignores not only all modifications, but also the multiplicity issue arising from the many-to-one comparison, and a Dunnett procedure that ignores modifications, but adjusts for the initially started multiple treatments. The maximum inflation of the type 1 error rate for such types of design can be calculated by searching for the "worst case" scenarios, that are sample size adaptation rules in the interim analysis that lead to the largest conditional type 1 error rate in any point of the sample space. To show the most extreme inflation, we initially assume unconstrained second stage sample size modifications leading to a large inflation of the type 1 error rate. Furthermore, we investigate the inflation when putting constraints on the second stage sample sizes. It turns out that, for example fixing the sample size of the control group, leads to designs controlling the type 1 error rate. © 2014 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prediction of final error level in learning and repetitive control

NASA Astrophysics Data System (ADS)

Levoci, Peter A.

Repetitive control (RC) is a field that creates controllers to eliminate the effects of periodic disturbances on a feedback control system. The methods have applications in spacecraft problems, to isolate fine pointing equipment from periodic vibration disturbances such as slight imbalances in momentum wheels or cryogenic pumps. A closely related field of control design is iterative learning control (ILC) which aims to eliminate tracking error in a task that repeats, each time starting from the same initial condition. Experiments done on a robot at NASA Langley Research Center showed that the final error levels produced by different candidate repetitive and learning controllers can be very different, even when each controller is analytically proven to converge to zero error in the deterministic case. Real world plant and measurement noise and quantization noise (from analog to digital and digital to analog converters) in these control methods are acted on as if they were error sources that will repeat and should be cancelled, which implies that the algorithms amplify such errors. Methods are developed that predict the final error levels of general first order ILC, of higher order ILC including current cycle learning, and of general RC, in the presence of noise, using frequency response methods. The method involves much less computation than the corresponding time domain approach that involves large matrices. The time domain approach was previously developed for ILC and handles a certain class of ILC methods. Here methods are created to include zero-phase filtering that is very important in creating practical designs. Also, time domain methods are developed for higher order ILC and for repetitive control. Since RC and ILC must be implemented digitally, all of these methods predict final error levels at the sample times. It is shown here that RC can easily converge to small error levels between sample times, but that ILC in most applications will have large and diverging intersample error if in fact zero error is reached at the sample times. This is independent of the ILC law used, and is purely a property of the physical system. Methods are developed to address this issue.

Comparison of structural and least-squares lines for estimating geologic relations

USGS Publications Warehouse

Williams, G.P.; Troutman, B.M.

1990-01-01

Two different goals in fitting straight lines to data are to estimate a "true" linear relation (physical law) and to predict values of the dependent variable with the smallest possible error. Regarding the first goal, a Monte Carlo study indicated that the structural-analysis (SA) method of fitting straight lines to data is superior to the ordinary least-squares (OLS) method for estimating "true" straight-line relations. Number of data points, slope and intercept of the true relation, and variances of the errors associated with the independent (X) and dependent (Y) variables influence the degree of agreement. For example, differences between the two line-fitting methods decrease as error in X becomes small relative to error in Y. Regarding the second goal-predicting the dependent variable-OLS is better than SA. Again, the difference diminishes as X takes on less error relative to Y. With respect to estimation of slope and intercept and prediction of Y, agreement between Monte Carlo results and large-sample theory was very good for sample sizes of 100, and fair to good for sample sizes of 20. The procedures and error measures are illustrated with two geologic examples. ?? 1990 International Association for Mathematical Geology.

What Do Phonological Processing Errors Tell about Students' Skills in Reading, Writing, and Oral Language?

ERIC Educational Resources Information Center

Choi, Dowon; Hatcher, Ryan C.; Dulong-Langley, Susan; Liu, Xiaochen; Bray, Melissa A.; Courville, Troy; O'Brien, Rebecca; DeBiase, Emily

2017-01-01

The kinds of errors that children and adolescents make on phonological processing tasks were studied with a large sample between ages 4 and 19 (N = 3,842) who were tested on the Kaufman Test of Educational Achievement-Third Edition (KTEA-3). Principal component analysis identified two phonological processing factors: Basic Phonological Awareness…

Accounting for Sampling Error in Genetic Eigenvalues Using Random Matrix Theory.

PubMed

Sztepanacz, Jacqueline L; Blows, Mark W

2017-07-01

The distribution of genetic variance in multivariate phenotypes is characterized by the empirical spectral distribution of the eigenvalues of the genetic covariance matrix. Empirical estimates of genetic eigenvalues from random effects linear models are known to be overdispersed by sampling error, where large eigenvalues are biased upward, and small eigenvalues are biased downward. The overdispersion of the leading eigenvalues of sample covariance matrices have been demonstrated to conform to the Tracy-Widom (TW) distribution. Here we show that genetic eigenvalues estimated using restricted maximum likelihood (REML) in a multivariate random effects model with an unconstrained genetic covariance structure will also conform to the TW distribution after empirical scaling and centering. However, where estimation procedures using either REML or MCMC impose boundary constraints, the resulting genetic eigenvalues tend not be TW distributed. We show how using confidence intervals from sampling distributions of genetic eigenvalues without reference to the TW distribution is insufficient protection against mistaking sampling error as genetic variance, particularly when eigenvalues are small. By scaling such sampling distributions to the appropriate TW distribution, the critical value of the TW statistic can be used to determine if the magnitude of a genetic eigenvalue exceeds the sampling error for each eigenvalue in the spectral distribution of a given genetic covariance matrix. Copyright © 2017 by the Genetics Society of America.

Frozen section analysis of margins for head and neck tumor resections: reduction of sampling errors with a third histologic level.

PubMed

Olson, Stephen M; Hussaini, Mohammad; Lewis, James S

2011-05-01

Frozen section analysis is an essential tool for assessing margins intra-operatively to assure complete resection. Many institutions evaluate surgical defect edge tissue provided by the surgeon after the main lesion has been removed. With the increasing use of transoral laser microsurgery, this method is becoming even more prevalent. We sought to evaluate error rates at our large academic institution and to see if sampling errors could be reduced by the simple method change of taking an additional third section on these specimens. All head and neck tumor resection cases from January 2005 through August 2008 with margins evaluated by frozen section were identified by database search. These cases were analyzed by cutting two levels during frozen section and a third permanent section later. All resection cases from August 2008 through July 2009 were identified as well. These were analyzed by cutting three levels during frozen section (the third a 'much deeper' level) and a fourth permanent section later. Error rates for both of these periods were determined. Errors were separated into sampling and interpretation types. There were 4976 total frozen section specimens from 848 patients. The overall error rate was 2.4% for all frozen sections where just two levels were evaluated and was 2.5% when three levels were evaluated (P=0.67). The sampling error rate was 1.6% for two-level sectioning and 1.2% for three-level sectioning (P=0.42). However, when considering only the frozen section cases where tumor was ultimately identified (either at the time of frozen section or on permanent sections) the sampling error rate for two-level sectioning was 15.3 versus 7.4% for three-level sectioning. This difference was statistically significant (P=0.006). Cutting a single additional 'deeper' level at the time of frozen section identifies more tumor-bearing specimens and may reduce the number of sampling errors.

Optimal number of features as a function of sample size for various classification rules.

PubMed

Hua, Jianping; Xiong, Zixiang; Lowey, James; Suh, Edward; Dougherty, Edward R

2005-04-15

Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically (but not always), for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The potential downside of using too many features is most critical for small samples, which are commonplace for gene-expression-based classifiers for phenotype discrimination. For fixed sample size and feature-label distribution, the issue is to find an optimal number of features. Since only in rare cases is there a known distribution of the error as a function of the number of features and sample size, this study employs simulation for various feature-label distributions and classification rules, and across a wide range of sample and feature-set sizes. To achieve the desired end, finding the optimal number of features as a function of sample size, it employs massively parallel computation. Seven classifiers are treated: 3-nearest-neighbor, Gaussian kernel, linear support vector machine, polynomial support vector machine, perceptron, regular histogram and linear discriminant analysis. Three Gaussian-based models are considered: linear, nonlinear and bimodal. In addition, real patient data from a large breast-cancer study is considered. To mitigate the combinatorial search for finding optimal feature sets, and to model the situation in which subsets of genes are co-regulated and correlation is internal to these subsets, we assume that the covariance matrix of the features is blocked, with each block corresponding to a group of correlated features. Altogether there are a large number of error surfaces for the many cases. These are provided in full on a companion website, which is meant to serve as resource for those working with small-sample classification. For the companion website, please visit http://public.tgen.org/tamu/ofs/ e-dougherty@ee.tamu.edu.

Efficient Solar Scene Wavefront Estimation with Reduced Systematic and RMS Errors: Summary

NASA Astrophysics Data System (ADS)

Anugu, N.; Garcia, P.

2016-04-01

Wave front sensing for solar telescopes is commonly implemented with the Shack-Hartmann sensors. Correlation algorithms are usually used to estimate the extended scene Shack-Hartmann sub-aperture image shifts or slopes. The image shift is computed by correlating a reference sub-aperture image with the target distorted sub-aperture image. The pixel position where the maximum correlation is located gives the image shift in integer pixel coordinates. Sub-pixel precision image shifts are computed by applying a peak-finding algorithm to the correlation peak Poyneer (2003); Löfdahl (2010). However, the peak-finding algorithm results are usually biased towards the integer pixels, these errors are called as systematic bias errors Sjödahl (1994). These errors are caused due to the low pixel sampling of the images. The amplitude of these errors depends on the type of correlation algorithm and the type of peak-finding algorithm being used. To study the systematic errors in detail, solar sub-aperture synthetic images are constructed by using a Swedish Solar Telescope solar granulation image1. The performance of cross-correlation algorithm in combination with different peak-finding algorithms is investigated. The studied peak-finding algorithms are: parabola Poyneer (2003); quadratic polynomial Löfdahl (2010); threshold center of gravity Bailey (2003); Gaussian Nobach & Honkanen (2005) and Pyramid Bailey (2003). The systematic error study reveals that that the pyramid fit is the most robust to pixel locking effects. The RMS error analysis study reveals that the threshold centre of gravity behaves better in low SNR, although the systematic errors in the measurement are large. It is found that no algorithm is best for both the systematic and the RMS error reduction. To overcome the above problem, a new solution is proposed. In this solution, the image sampling is increased prior to the actual correlation matching. The method is realized in two steps to improve its computational efficiency. In the first step, the cross-correlation is implemented at the original image spatial resolution grid (1 pixel). In the second step, the cross-correlation is performed using a sub-pixel level grid by limiting the field of search to 4 × 4 pixels centered at the first step delivered initial position. The generation of these sub-pixel grid based region of interest images is achieved with the bi-cubic interpolation. The correlation matching with sub-pixel grid technique was previously reported in electronic speckle photography Sjö'dahl (1994). This technique is applied here for the solar wavefront sensing. A large dynamic range and a better accuracy in the measurements are achieved with the combination of the original pixel grid based correlation matching in a large field of view and a sub-pixel interpolated image grid based correlation matching within a small field of view. The results revealed that the proposed method outperforms all the different peak-finding algorithms studied in the first approach. It reduces both the systematic error and the RMS error by a factor of 5 (i.e., 75% systematic error reduction), when 5 times improved image sampling was used. This measurement is achieved at the expense of twice the computational cost. With the 5 times improved image sampling, the wave front accuracy is increased by a factor of 5. The proposed solution is strongly recommended for wave front sensing in the solar telescopes, particularly, for measuring large dynamic image shifts involved open loop adaptive optics. Also, by choosing an appropriate increment of image sampling in trade-off between the computational speed limitation and the aimed sub-pixel image shift accuracy, it can be employed in closed loop adaptive optics. The study is extended to three other class of sub-aperture images (a point source; a laser guide star; a Galactic Center extended scene). The results are planned to submit for the Optical Express journal.

Comparison of Efficiency of Jackknife and Variance Component Estimators of Standard Errors. Program Statistics Research. Technical Report.

ERIC Educational Resources Information Center

Longford, Nicholas T.

Large scale surveys usually employ a complex sampling design and as a consequence, no standard methods for estimation of the standard errors associated with the estimates of population means are available. Resampling methods, such as jackknife or bootstrap, are often used, with reference to their properties of robustness and reduction of bias. A…

Evaluation of the depth-integration method of measuring water discharge in large rivers

USGS Publications Warehouse

Moody, J.A.; Troutman, B.M.

1992-01-01

The depth-integration method oor measuring water discharge makes a continuos measurement of the water velocity from the water surface to the bottom at 20 to 40 locations or verticals across a river. It is especially practical for large rivers where river traffic makes it impractical to use boats attached to taglines strung across the river or to use current meters suspended from bridges. This method has the additional advantage over the standard two- and eight-tenths method in that a discharge-weighted suspended-sediment sample can be collected at the same time. When this method is used in large rivers such as the Missouri, Mississippi and Ohio, a microwave navigation system is used to determine the ship's position at each vertical sampling location across the river, and to make accurate velocity corrections to compensate for shift drift. An essential feature is a hydraulic winch that can lower and raise the current meter at a constant transit velocity so that the velocities at all depths are measured for equal lengths of time. Field calibration measurements show that: (1) the mean velocity measured on the upcast (bottom to surface) is within 1% of the standard mean velocity determined by 9-11 point measurements; (2) if the transit velocity is less than 25% of the mean velocity, then average error in the mean velocity is 4% or less. The major source of bias error is a result of mounting the current meter above a sounding weight and sometimes above a suspended-sediment sampling bottle, which prevents measurement of the velocity all the way to the bottom. The measured mean velocity is slightly larger than the true mean velocity. This bias error in the discharge is largest in shallow water (approximately 8% for the Missouri River at Hermann, MO, where the mean depth was 4.3 m) and smallest in deeper water (approximately 3% for the Mississippi River at Vickbsurg, MS, where the mean depth was 14.5 m). The major source of random error in the discharge is the natural variability of river velocities, which we assumed to be independent and random at each vertical. The standard error of the estimated mean velocity, at an individual vertical sampling location, may be as large as 9%, for large sand-bed alluvial rivers. The computed discharge, however, is a weighted mean of these random velocities. Consequently the standard error of computed discharge is divided by the square root of the number of verticals, producing typical values between 1 and 2%. The discharges measured by the depth-integrated method agreed within ??5% of those measured simultaneously by the standard two- and eight-tenths, six-tenth and moving boat methods. ?? 1992.

Predictability of Circulation Transitions (Observed and Modeled): Non-diffusive Dynamics, Markov Chains and Error Growth.

NASA Astrophysics Data System (ADS)

Straus, D. M.

2006-12-01

The transitions between portions of the state space of the large-scale flow is studied from daily wintertime data over the Pacific North America region using the NCEP reanalysis data set (54 winters) and very large suites of hindcasts made with the COLA atmospheric GCM with observed SST (55 members for each of 18 winters). The partition of the large-scale state space is guided by cluster analysis, whose statistical significance and relationship to SST is reviewed (Straus and Molteni, 2004; Straus, Corti and Molteni, 2006). The determination of the global nature of the flow through state space is studied using Markov Chains (Crommelin, 2004). In particular the non-diffusive part of the flow is contrasted in nature (small data sample) and the AGCM (large data sample). The intrinsic error growth associated with different portions of the state space is studied through sets of identical twin AGCM simulations. The goal is to obtain realistic estimates of predictability times for large-scale transitions that should be useful in long-range forecasting.

Divergent estimation error in portfolio optimization and in linear regression

NASA Astrophysics Data System (ADS)

Kondor, I.; Varga-Haszonits, I.

2008-08-01

The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifestation of an algorithmic phase transition, it is accompanied by a number of critical phenomena, and displays universality. As the structure of a large number of multidimensional regression and modelling problems is very similar to portfolio optimization, the scope of the above observations extends far beyond finance, and covers a large number of problems in operations research, machine learning, bioinformatics, medical science, economics, and technology.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 1; Improved Method and Uncertainties

NASA Technical Reports Server (NTRS)

Olson, William S.; Kummerow, Christian D.; Yang, Song; Petty, Grant W.; Tao, Wei-Kuo; Bell, Thomas L.; Braun, Scott A.; Wang, Yansen; Lang, Stephen E.; Johnson, Daniel E.;

A New Stratified Sampling Procedure which Decreases Error Estimation of Varroa Mite Number on Sticky Boards.

PubMed

Kretzschmar, A; Durand, E; Maisonnasse, A; Vallon, J; Le Conte, Y

2015-06-01

A new procedure of stratified sampling is proposed in order to establish an accurate estimation of Varroa destructor populations on sticky bottom boards of the hive. It is based on the spatial sampling theory that recommends using regular grid stratification in the case of spatially structured process. The distribution of varroa mites on sticky board being observed as spatially structured, we designed a sampling scheme based on a regular grid with circles centered on each grid element. This new procedure is then compared with a former method using partially random sampling. Relative error improvements are exposed on the basis of a large sample of simulated sticky boards (n=20,000) which provides a complete range of spatial structures, from a random structure to a highly frame driven structure. The improvement of varroa mite number estimation is then measured by the percentage of counts with an error greater than a given level. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Learning from Past Classification Errors: Exploring Methods for Improving the Performance of a Deep Learning-based Building Extraction Model through Quantitative Analysis of Commission Errors for Optimal Sample Selection

NASA Astrophysics Data System (ADS)

Swan, B.; Laverdiere, M.; Yang, L.

2017-12-01

In the past five years, deep Convolutional Neural Networks (CNN) have been increasingly favored for computer vision applications due to their high accuracy and ability to generalize well in very complex problems; however, details of how they function and in turn how they may be optimized are still imperfectly understood. In particular, their complex and highly nonlinear network architecture, including many hidden layers and self-learned parameters, as well as their mathematical implications, presents open questions about how to effectively select training data. Without knowledge of the exact ways the model processes and transforms its inputs, intuition alone may fail as a guide to selecting highly relevant training samples. Working in the context of improving a CNN-based building extraction model used for the LandScan USA gridded population dataset, we have approached this problem by developing a semi-supervised, highly-scalable approach to select training samples from a dataset of identified commission errors. Due to the large scope this project, tens of thousands of potential samples could be derived from identified commission errors. To efficiently trim those samples down to a manageable and effective set for creating additional training sample, we statistically summarized the spectral characteristics of areas with rates of commission errors at the image tile level and grouped these tiles using affinity propagation. Highly representative members of each commission error cluster were then used to select sites for training sample creation. The model will be incrementally re-trained with the new training data to allow for an assessment of how the addition of different types of samples affects the model performance, such as precision and recall rates. By using quantitative analysis and data clustering techniques to select highly relevant training samples, we hope to improve model performance in a manner that is resource efficient, both in terms of training process and in sample creation.

A pharmacometric case study regarding the sensitivity of structural model parameter estimation to error in patient reported dosing times.

PubMed

Knights, Jonathan; Rohatagi, Shashank

2015-12-01

Although there is a body of literature focused on minimizing the effect of dosing inaccuracies on pharmacokinetic (PK) parameter estimation, most of the work centers on missing doses. No attempt has been made to specifically characterize the effect of error in reported dosing times. Additionally, existing work has largely dealt with cases in which the compound of interest is dosed at an interval no less than its terminal half-life. This work provides a case study investigating how error in patient reported dosing times might affect the accuracy of structural model parameter estimation under sparse sampling conditions when the dosing interval is less than the terminal half-life of the compound, and the underlying kinetics are monoexponential. Additional effects due to noncompliance with dosing events are not explored and it is assumed that the structural model and reasonable initial estimates of the model parameters are known. Under the conditions of our simulations, with structural model CV % ranging from ~20 to 60 %, parameter estimation inaccuracy derived from error in reported dosing times was largely controlled around 10 % on average. Given that no observed dosing was included in the design and sparse sampling was utilized, we believe these error results represent a practical ceiling given the variability and parameter estimates for the one-compartment model. The findings suggest additional investigations may be of interest and are noteworthy given the inability of current PK software platforms to accommodate error in dosing times.

The algorithm study for using the back propagation neural network in CT image segmentation

NASA Astrophysics Data System (ADS)

Zhang, Peng; Liu, Jie; Chen, Chen; Li, Ying Qi

2017-01-01

Back propagation neural network(BP neural network) is a type of multi-layer feed forward network which spread positively, while the error spread backwardly. Since BP network has advantages in learning and storing the mapping between a large number of input and output layers without complex mathematical equations to describe the mapping relationship, it is most widely used. BP can iteratively compute the weight coefficients and thresholds of the network based on the training and back propagation of samples, which can minimize the error sum of squares of the network. Since the boundary of the computed tomography (CT) heart images is usually discontinuous, and it exist large changes in the volume and boundary of heart images, The conventional segmentation such as region growing and watershed algorithm can't achieve satisfactory results. Meanwhile, there are large differences between the diastolic and systolic images. The conventional methods can't accurately classify the two cases. In this paper, we introduced BP to handle the segmentation of heart images. We segmented a large amount of CT images artificially to obtain the samples, and the BP network was trained based on these samples. To acquire the appropriate BP network for the segmentation of heart images, we normalized the heart images, and extract the gray-level information of the heart. Then the boundary of the images was input into the network to compare the differences between the theoretical output and the actual output, and we reinput the errors into the BP network to modify the weight coefficients of layers. Through a large amount of training, the BP network tend to be stable, and the weight coefficients of layers can be determined, which means the relationship between the CT images and the boundary of heart.

Analytic score distributions for a spatially continuous tridirectional Monte Carol transport problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Booth, T.E.

1996-01-01

The interpretation of the statistical error estimates produced by Monte Carlo transport codes is still somewhat of an art. Empirically, there are variance reduction techniques whose error estimates are almost always reliable, and there are variance reduction techniques whose error estimates are often unreliable. Unreliable error estimates usually result from inadequate large-score sampling from the score distribution`s tail. Statisticians believe that more accurate confidence interval statements are possible if the general nature of the score distribution can be characterized. Here, the analytic score distribution for the exponential transform applied to a simple, spatially continuous Monte Carlo transport problem is provided.more » Anisotropic scattering and implicit capture are included in the theory. In large part, the analytic score distributions that are derived provide the basis for the ten new statistical quality checks in MCNP.« less

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Violation of the Sphericity Assumption and Its Effect on Type-I Error Rates in Repeated Measures ANOVA and Multi-Level Linear Models (MLM).

PubMed

Haverkamp, Nicolas; Beauducel, André

2017-01-01

We investigated the effects of violations of the sphericity assumption on Type I error rates for different methodical approaches of repeated measures analysis using a simulation approach. In contrast to previous simulation studies on this topic, up to nine measurement occasions were considered. Effects of the level of inter-correlations between measurement occasions on Type I error rates were considered for the first time. Two populations with non-violation of the sphericity assumption, one with uncorrelated measurement occasions and one with moderately correlated measurement occasions, were generated. One population with violation of the sphericity assumption combines uncorrelated with highly correlated measurement occasions. A second population with violation of the sphericity assumption combines moderately correlated and highly correlated measurement occasions. From these four populations without any between-group effect or within-subject effect 5,000 random samples were drawn. Finally, the mean Type I error rates for Multilevel linear models (MLM) with an unstructured covariance matrix (MLM-UN), MLM with compound-symmetry (MLM-CS) and for repeated measures analysis of variance (rANOVA) models (without correction, with Greenhouse-Geisser-correction, and Huynh-Feldt-correction) were computed. To examine the effect of both the sample size and the number of measurement occasions, sample sizes of n = 20, 40, 60, 80, and 100 were considered as well as measurement occasions of m = 3, 6, and 9. With respect to rANOVA, the results plead for a use of rANOVA with Huynh-Feldt-correction, especially when the sphericity assumption is violated, the sample size is rather small and the number of measurement occasions is large. For MLM-UN, the results illustrate a massive progressive bias for small sample sizes ( n = 20) and m = 6 or more measurement occasions. This effect could not be found in previous simulation studies with a smaller number of measurement occasions. The proportionality of bias and number of measurement occasions should be considered when MLM-UN is used. The good news is that this proportionality can be compensated by means of large sample sizes. Accordingly, MLM-UN can be recommended even for small sample sizes for about three measurement occasions and for large sample sizes for about nine measurement occasions.

Measuring discharge with ADCPs: Inferences from synthetic velocity profiles

USGS Publications Warehouse

Rehmann, C.R.; Mueller, D.S.; Oberg, K.A.

2009-01-01

Synthetic velocity profiles are used to determine guidelines for sampling discharge with acoustic Doppler current profilers (ADCPs). The analysis allows the effects of instrument characteristics, sampling parameters, and properties of the flow to be studied systematically. For mid-section measurements, the averaging time required for a single profile measurement always exceeded the 40 s usually recommended for velocity measurements, and it increased with increasing sample interval and increasing time scale of the large eddies. Similarly, simulations of transect measurements show that discharge error decreases as the number of large eddies sampled increases. The simulations allow sampling criteria that account for the physics of the flow to be developed. ?? 2009 ASCE.

Modification of the Mantel-Haenszel and Logistic Regression DIF Procedures to Incorporate the SIBTEST Regression Correction

ERIC Educational Resources Information Center

DeMars, Christine E.

2009-01-01

The Mantel-Haenszel (MH) and logistic regression (LR) differential item functioning (DIF) procedures have inflated Type I error rates when there are large mean group differences, short tests, and large sample sizes.When there are large group differences in mean score, groups matched on the observed number-correct score differ on true score,…

Measurements of stem diameter: implications for individual- and stand-level errors.

PubMed

Paul, Keryn I; Larmour, John S; Roxburgh, Stephen H; England, Jacqueline R; Davies, Micah J; Luck, Hamish D

2017-08-01

Stem diameter is one of the most common measurements made to assess the growth of woody vegetation, and the commercial and environmental benefits that it provides (e.g. wood or biomass products, carbon sequestration, landscape remediation). Yet inconsistency in its measurement is a continuing source of error in estimates of stand-scale measures such as basal area, biomass, and volume. Here we assessed errors in stem diameter measurement through repeated measurements of individual trees and shrubs of varying size and form (i.e. single- and multi-stemmed) across a range of contrasting stands, from complex mixed-species plantings to commercial single-species plantations. We compared a standard diameter tape with a Stepped Diameter Gauge (SDG) for time efficiency and measurement error. Measurement errors in diameter were slightly (but significantly) influenced by size and form of the tree or shrub, and stem height at which the measurement was made. Compared to standard tape measurement, the mean systematic error with SDG measurement was only -0.17 cm, but varied between -0.10 and -0.52 cm. Similarly, random error was relatively large, with standard deviations (and percentage coefficients of variation) averaging only 0.36 cm (and 3.8%), but varying between 0.14 and 0.61 cm (and 1.9 and 7.1%). However, at the stand scale, sampling errors (i.e. how well individual trees or shrubs selected for measurement of diameter represented the true stand population in terms of the average and distribution of diameter) generally had at least a tenfold greater influence on random errors in basal area estimates than errors in diameter measurements. This supports the use of diameter measurement tools that have high efficiency, such as the SDG. Use of the SDG almost halved the time required for measurements compared to the diameter tape. Based on these findings, recommendations include the following: (i) use of a tape to maximise accuracy when developing allometric models, or when monitoring relatively small changes in permanent sample plots (e.g. National Forest Inventories), noting that care is required in irregular-shaped, large-single-stemmed individuals, and (ii) use of a SDG to maximise efficiency when using inventory methods to assess basal area, and hence biomass or wood volume, at the stand scale (i.e. in studies of impacts of management or site quality) where there are budgetary constraints, noting the importance of sufficient sample sizes to ensure that the population sampled represents the true population.

Assessment and mitigation of errors associated with a large-scale field investigation of methane emissions from the Marcellus Shale

NASA Astrophysics Data System (ADS)

Caulton, D.; Golston, L.; Li, Q.; Bou-Zeid, E.; Pan, D.; Lane, H.; Lu, J.; Fitts, J. P.; Zondlo, M. A.

2015-12-01

Recent work suggests the distribution of methane emissions from fracking operations is a skewed distributed with a small percentage of emitters contributing a large proportion of the total emissions. In order to provide a statistically robust distributions of emitters and determine the presence of super-emitters, errors in current techniques need to be constrained and mitigated. The Marcellus shale, the most productive natural gas shale field in the United States, has received less intense focus for well-level emissions and is here investigated to provide the distribution of methane emissions. In July of 2015 approximately 250 unique well pads were sampled using the Princeton Atmospheric Chemistry Mobile Acquisition Node (PAC-MAN). This mobile lab includes a Garmin GPS unit, Vaisala weather station (WTX520), LICOR 7700 CH4 open path sensor and LICOR 7500 CO2/H2O open path sensor. Sampling sites were preselected based on wind direction, sampling distance and elevation grade. All sites were sampled during low boundary layer conditions (600-1000 and 1800-2200 local time). The majority of sites were sampled 1-3 times while selected test sites were sampled multiple times or resampled several times during the day. For selected sites a sampling tower was constructed consisting of a Metek uSonic-3 Class A sonic anemometer, and an additional LICOR 7700 and 7500. Data were recorded for at least one hour at these sites. A robust study and inter-comparison of different methodologies will be presented. The Gaussian plume model will be used to calculate fluxes for all sites and compare results from test sites with multiple passes. Tower data is used to provide constraints on the Gaussian plume model. Additionally, Large Eddy Simulation (LES) modeling will be used to calculate emissions from the tower sites. Alternative techniques will also be discussed. Results from these techniques will be compared to identify best practices and provide robust error estimates.

Sample sizes to control error estimates in determining soil bulk density in California forest soils

Treesearch

Youzhi Han; Jianwei Zhang; Kim G. Mattson; Weidong Zhang; Thomas A. Weber

2016-01-01

Characterizing forest soil properties with high variability is challenging, sometimes requiring large numbers of soil samples. Soil bulk density is a standard variable needed along with element concentrations to calculate nutrient pools. This study aimed to determine the optimal sample size, the number of observation (n), for predicting the soil bulk density with a...

Accuracy assessment in the Large Area Crop Inventory Experiment

NASA Technical Reports Server (NTRS)

Houston, A. G.; Pitts, D. E.; Feiveson, A. H.; Badhwar, G.; Ferguson, M.; Hsu, E.; Potter, J.; Chhikara, R.; Rader, M.; Ahlers, C.

1979-01-01

The Accuracy Assessment System (AAS) of the Large Area Crop Inventory Experiment (LACIE) was responsible for determining the accuracy and reliability of LACIE estimates of wheat production, area, and yield, made at regular intervals throughout the crop season, and for investigating the various LACIE error sources, quantifying these errors, and relating them to their causes. Some results of using the AAS during the three years of LACIE are reviewed. As the program culminated, AAS was able not only to meet the goal of obtaining accurate statistical estimates of sampling and classification accuracy, but also the goal of evaluating component labeling errors. Furthermore, the ground-truth data processing matured from collecting data for one crop (small grains) to collecting, quality-checking, and archiving data for all crops in a LACIE small segment.

Sample presentation, sources of error and future perspectives on the application of vibrational spectroscopy in the wine industry.

PubMed

Cozzolino, Daniel

2015-03-30

Vibrational spectroscopy encompasses a number of techniques and methods including ultra-violet, visible, Fourier transform infrared or mid infrared, near infrared and Raman spectroscopy. The use and application of spectroscopy generates spectra containing hundreds of variables (absorbances at each wavenumbers or wavelengths), resulting in the production of large data sets representing the chemical and biochemical wine fingerprint. Multivariate data analysis techniques are then required to handle the large amount of data generated in order to interpret the spectra in a meaningful way in order to develop a specific application. This paper focuses on the developments of sample presentation and main sources of error when vibrational spectroscopy methods are applied in wine analysis. Recent and novel applications will be discussed as examples of these developments. © 2014 Society of Chemical Industry.

RECONSTRUCTING REDSHIFT DISTRIBUTIONS WITH CROSS-CORRELATIONS: TESTS AND AN OPTIMIZED RECIPE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matthews, Daniel J.; Newman, Jeffrey A., E-mail: djm70@pitt.ed, E-mail: janewman@pitt.ed

2010-09-20

Many of the cosmological tests to be performed by planned dark energy experiments will require extremely well-characterized photometric redshift measurements. Current estimates for cosmic shear are that the true mean redshift of the objects in each photo-z bin must be known to better than 0.002(1 + z), and the width of the bin must be known to {approx}0.003(1 + z) if errors in cosmological measurements are not to be degraded significantly. A conventional approach is to calibrate these photometric redshifts with large sets of spectroscopic redshifts. However, at the depths probed by Stage III surveys (such as DES), let alonemore » Stage IV (LSST, JDEM, and Euclid), existing large redshift samples have all been highly (25%-60%) incomplete, with a strong dependence of success rate on both redshift and galaxy properties. A powerful alternative approach is to exploit the clustering of galaxies to perform photometric redshift calibrations. Measuring the two-point angular cross-correlation between objects in some photometric redshift bin and objects with known spectroscopic redshift, as a function of the spectroscopic z, allows the true redshift distribution of a photometric sample to be reconstructed in detail, even if it includes objects too faint for spectroscopy or if spectroscopic samples are highly incomplete. We test this technique using mock DEEP2 Galaxy Redshift survey light cones constructed from the Millennium Simulation semi-analytic galaxy catalogs. From this realistic test, which incorporates the effects of galaxy bias evolution and cosmic variance, we find that the true redshift distribution of a photometric sample can, in fact, be determined accurately with cross-correlation techniques. We also compare the empirical error in the reconstruction of redshift distributions to previous analytic predictions, finding that additional components must be included in error budgets to match the simulation results. This extra error contribution is small for surveys that sample large areas of sky (>{approx}10{sup 0}-100{sup 0}), but dominant for {approx}1 deg{sup 2} fields. We conclude by presenting a step-by-step, optimized recipe for reconstructing redshift distributions from cross-correlation information using standard correlation measurements.« less

A video multitracking system for quantification of individual behavior in a large fish shoal: advantages and limits.

PubMed

Delcourt, Johann; Becco, Christophe; Vandewalle, Nicolas; Poncin, Pascal

2009-02-01

The capability of a new multitracking system to track a large number of unmarked fish (up to 100) is evaluated. This system extrapolates a trajectory from each individual and analyzes recorded sequences that are several minutes long. This system is very efficient in statistical individual tracking, where the individual's identity is important for a short period of time in comparison with the duration of the track. Individual identification is typically greater than 99%. Identification is largely efficient (more than 99%) when the fish images do not cross the image of a neighbor fish. When the images of two fish merge (occlusion), we consider that the spot on the screen has a double identity. Consequently, there are no identification errors during occlusions, even though the measurement of the positions of each individual is imprecise. When the images of these two merged fish separate (separation), individual identification errors are more frequent, but their effect is very low in statistical individual tracking. On the other hand, in complete individual tracking, where individual fish identity is important for the entire trajectory, each identification error invalidates the results. In such cases, the experimenter must observe whether the program assigns the correct identification, and, when an error is made, must edit the results. This work is not too costly in time because it is limited to the separation events, accounting for fewer than 0.1% of individual identifications. Consequently, in both statistical and rigorous individual tracking, this system allows the experimenter to gain time by measuring the individual position automatically. It can also analyze the structural and dynamic properties of an animal group with a very large sample, with precision and sampling that are impossible to obtain with manual measures.

Simultaneous Control of Error Rates in fMRI Data Analysis

PubMed Central

Kang, Hakmook; Blume, Jeffrey; Ombao, Hernando; Badre, David

2015-01-01

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of human brain imaging data, the number of comparisons is so large that this solution breaks down: the local Type II error rate ends up being so large that scientifically meaningful analysis is precluded. Here we propose a novel solution to this problem: allow the Type I error rate to converge to zero along with the Type II error rate. It works because when the Type I error rate per comparison is very small, the accumulation (or global) Type I error rate is also small. This solution is achieved by employing the Likelihood paradigm, which uses likelihood ratios to measure the strength of evidence on a voxel-by-voxel basis. In this paper, we provide theoretical and empirical justification for a likelihood approach to the analysis of human brain imaging data. In addition, we present extensive simulations that show the likelihood approach is viable, leading to ‘cleaner’ looking brain maps and operationally superiority (lower average error rate). Finally, we include a case study on cognitive control related activation in the prefrontal cortex of the human brain. PMID:26272730

The observed clustering of damaging extra-tropical cyclones in Europe

NASA Astrophysics Data System (ADS)

Cusack, S.

2015-12-01

The clustering of severe European windstorms on annual timescales has substantial impacts on the re/insurance industry. Management of the risk is impaired by large uncertainties in estimates of clustering from historical storm datasets typically covering the past few decades. The uncertainties are unusually large because clustering depends on the variance of storm counts. Eight storm datasets are gathered for analysis in this study in order to reduce these uncertainties. Six of the datasets contain more than 100~years of severe storm information to reduce sampling errors, and the diversity of information sources and analysis methods between datasets sample observational errors. All storm severity measures used in this study reflect damage, to suit re/insurance applications. It is found that the shortest storm dataset of 42 years in length provides estimates of clustering with very large sampling and observational errors. The dataset does provide some useful information: indications of stronger clustering for more severe storms, particularly for southern countries off the main storm track. However, substantially different results are produced by removal of one stormy season, 1989/1990, which illustrates the large uncertainties from a 42-year dataset. The extended storm records place 1989/1990 into a much longer historical context to produce more robust estimates of clustering. All the extended storm datasets show a greater degree of clustering with increasing storm severity and suggest clustering of severe storms is much more material than weaker storms. Further, they contain signs of stronger clustering in areas off the main storm track, and weaker clustering for smaller-sized areas, though these signals are smaller than uncertainties in actual values. Both the improvement of existing storm records and development of new historical storm datasets would help to improve management of this risk.

Outlier Removal and the Relation with Reporting Errors and Quality of Psychological Research

PubMed Central

Bakker, Marjan; Wicherts, Jelte M.

2014-01-01

Background The removal of outliers to acquire a significant result is a questionable research practice that appears to be commonly used in psychology. In this study, we investigated whether the removal of outliers in psychology papers is related to weaker evidence (against the null hypothesis of no effect), a higher prevalence of reporting errors, and smaller sample sizes in these papers compared to papers in the same journals that did not report the exclusion of outliers from the analyses. Methods and Findings We retrieved a total of 2667 statistical results of null hypothesis significance tests from 153 articles in main psychology journals, and compared results from articles in which outliers were removed (N = 92) with results from articles that reported no exclusion of outliers (N = 61). We preregistered our hypotheses and methods and analyzed the data at the level of articles. Results show no significant difference between the two types of articles in median p value, sample sizes, or prevalence of all reporting errors, large reporting errors, and reporting errors that concerned the statistical significance. However, we did find a discrepancy between the reported degrees of freedom of t tests and the reported sample size in 41% of articles that did not report removal of any data values. This suggests common failure to report data exclusions (or missingness) in psychological articles. Conclusions We failed to find that the removal of outliers from the analysis in psychological articles was related to weaker evidence (against the null hypothesis of no effect), sample size, or the prevalence of errors. However, our control sample might be contaminated due to nondisclosure of excluded values in articles that did not report exclusion of outliers. Results therefore highlight the importance of more transparent reporting of statistical analyses. PMID:25072606

Assessment of Systematic Chromatic Errors that Impact Sub-1% Photometric Precision in Large-Area Sky Surveys

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, T. S.

Meeting the science goals for many current and future ground-based optical large-area sky surveys requires that the calibrated broadband photometry is stable in time and uniform over the sky to 1% precision or better. Past surveys have achieved photometric precision of 1-2% by calibrating the survey's stellar photometry with repeated measurements of a large number of stars observed in multiple epochs. The calibration techniques employed by these surveys only consider the relative frame-by-frame photometric zeropoint offset and the focal plane position-dependent illumination corrections, which are independent of the source color. However, variations in the wavelength dependence of the atmospheric transmissionmore » and the instrumental throughput induce source color-dependent systematic errors. These systematic errors must also be considered to achieve the most precise photometric measurements. In this paper, we examine such systematic chromatic errors using photometry from the Dark Energy Survey (DES) as an example. We define a natural magnitude system for DES and calculate the systematic errors on stellar magnitudes, when the atmospheric transmission and instrumental throughput deviate from the natural system. We conclude that the systematic chromatic errors caused by the change of airmass in each exposure, the change of the precipitable water vapor and aerosol in the atmosphere over time, and the non-uniformity of instrumental throughput over the focal plane, can be up to 2% in some bandpasses. We compare the calculated systematic chromatic errors with the observed DES data. For the test sample data, we correct these errors using measurements of the atmospheric transmission and instrumental throughput. The residual after correction is less than 0.3%. We also find that the errors for non-stellar objects are redshift-dependent and can be larger than those for stars at certain redshifts.« less

Potential, velocity, and density fields from sparse and noisy redshift-distance samples - Method

NASA Technical Reports Server (NTRS)

Dekel, Avishai; Bertschinger, Edmund; Faber, Sandra M.

1990-01-01

A method for recovering the three-dimensional potential, velocity, and density fields from large-scale redshift-distance samples is described. Galaxies are taken as tracers of the velocity field, not of the mass. The density field and the initial conditions are calculated using an iterative procedure that applies the no-vorticity assumption at an initial time and uses the Zel'dovich approximation to relate initial and final positions of particles on a grid. The method is tested using a cosmological N-body simulation 'observed' at the positions of real galaxies in a redshift-distance sample, taking into account their distance measurement errors. Malmquist bias and other systematic and statistical errors are extensively explored using both analytical techniques and Monte Carlo simulations.

Accuracy of accommodation in heterophoric patients: testing an interaction model in a large clinical sample.

PubMed

Hasebe, Satoshi; Nonaka, Fumitaka; Ohtsuki, Hiroshi

2005-11-01

A model of the cross-link interactions between accommodation and convergence predicted that heterophoria can induce large accommodation errors (Schor, Ophthalmic Physiol. Opt. 1999;19:134-150). In 99 consecutive patients with intermittent tropia or decompensated phoria, we tested these interactions by comparing their accommodative responses to a 2.50-D target under binocular fused conditions (BFC) and monocular occluded conditions (MOC). The accommodative response in BFC frequently differed from that in MOC. The magnitude of the accommodative errors in BFC, ranging from an accommodative lag of 1.80 D (in an esophoric patient) to an accommodative lead of 1.56 D (in an exophoric patient), was correlated with distance heterophoria and uncorrected refractive errors. These results indicate that heterophoria affects the accuracy of accommodation to various degrees, as the model predicted, and that an accommodative error larger than the depth of focus of the eye occurs in exchange for binocular single vision in some heterophoric patients.

The theory precision analyse of RFM localization of satellite remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhang, Jianqing; Xv, Biao

2009-11-01

The tradition method of detecting precision of Rational Function Model(RFM) is to make use of a great deal check points, and it calculates mean square error through comparing calculational coordinate with known coordinate. This method is from theory of probability, through a large number of samples to statistic estimate value of mean square error, we can think its estimate value approaches in its true when samples are well enough. This paper is from angle of survey adjustment, take law of propagation of error as the theory basis, and it calculates theory precision of RFM localization. Then take the SPOT5 three array imagery as experiment data, and the result of traditional method and narrated method in the paper are compared, while has confirmed tradition method feasible, and answered its theory precision question from the angle of survey adjustment.

Importance of anthropogenic climate impact, sampling error and urban development in sewer system design.

PubMed

Egger, C; Maurer, M

2015-04-15

Urban drainage design relying on observed precipitation series neglects the uncertainties associated with current and indeed future climate variability. Urban drainage design is further affected by the large stochastic variability of precipitation extremes and sampling errors arising from the short observation periods of extreme precipitation. Stochastic downscaling addresses anthropogenic climate impact by allowing relevant precipitation characteristics to be derived from local observations and an ensemble of climate models. This multi-climate model approach seeks to reflect the uncertainties in the data due to structural errors of the climate models. An ensemble of outcomes from stochastic downscaling allows for addressing the sampling uncertainty. These uncertainties are clearly reflected in the precipitation-runoff predictions of three urban drainage systems. They were mostly due to the sampling uncertainty. The contribution of climate model uncertainty was found to be of minor importance. Under the applied greenhouse gas emission scenario (A1B) and within the period 2036-2065, the potential for urban flooding in our Swiss case study is slightly reduced on average compared to the reference period 1981-2010. Scenario planning was applied to consider urban development associated with future socio-economic factors affecting urban drainage. The impact of scenario uncertainty was to a large extent found to be case-specific, thus emphasizing the need for scenario planning in every individual case. The results represent a valuable basis for discussions of new drainage design standards aiming specifically to include considerations of uncertainty. Copyright © 2015 Elsevier Ltd. All rights reserved.

Statistical theory and methodology for remote sensing data analysis

NASA Technical Reports Server (NTRS)

Odell, P. L.

1974-01-01

A model is developed for the evaluation of acreages (proportions) of different crop-types over a geographical area using a classification approach and methods for estimating the crop acreages are given. In estimating the acreages of a specific croptype such as wheat, it is suggested to treat the problem as a two-crop problem: wheat vs. nonwheat, since this simplifies the estimation problem considerably. The error analysis and the sample size problem is investigated for the two-crop approach. Certain numerical results for sample sizes are given for a JSC-ERTS-1 data example on wheat identification performance in Hill County, Montana and Burke County, North Dakota. Lastly, for a large area crop acreages inventory a sampling scheme is suggested for acquiring sample data and the problem of crop acreage estimation and the error analysis is discussed.

On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo

NASA Astrophysics Data System (ADS)

Icardi, Matteo; Boccardo, Gianluca; Tempone, Raúl

2016-09-01

A fast method with tunable accuracy is proposed to estimate errors and uncertainties in pore-scale and Digital Rock Physics (DRP) problems. The overall predictivity of these studies can be, in fact, hindered by many factors including sample heterogeneity, computational and imaging limitations, model inadequacy and not perfectly known physical parameters. The typical objective of pore-scale studies is the estimation of macroscopic effective parameters such as permeability, effective diffusivity and hydrodynamic dispersion. However, these are often non-deterministic quantities (i.e., results obtained for specific pore-scale sample and setup are not totally reproducible by another ;equivalent; sample and setup). The stochastic nature can arise due to the multi-scale heterogeneity, the computational and experimental limitations in considering large samples, and the complexity of the physical models. These approximations, in fact, introduce an error that, being dependent on a large number of complex factors, can be modeled as random. We propose a general simulation tool, based on multilevel Monte Carlo, that can reduce drastically the computational cost needed for computing accurate statistics of effective parameters and other quantities of interest, under any of these random errors. This is, to our knowledge, the first attempt to include Uncertainty Quantification (UQ) in pore-scale physics and simulation. The method can also provide estimates of the discretization error and it is tested on three-dimensional transport problems in heterogeneous materials, where the sampling procedure is done by generation algorithms able to reproduce realistic consolidated and unconsolidated random sphere and ellipsoid packings and arrangements. A totally automatic workflow is developed in an open-source code [1], that include rigid body physics and random packing algorithms, unstructured mesh discretization, finite volume solvers, extrapolation and post-processing techniques. The proposed method can be efficiently used in many porous media applications for problems such as stochastic homogenization/upscaling, propagation of uncertainty from microscopic fluid and rock properties to macro-scale parameters, robust estimation of Representative Elementary Volume size for arbitrary physics.

The observed clustering of damaging extratropical cyclones in Europe

NASA Astrophysics Data System (ADS)

Cusack, Stephen

2016-04-01

The clustering of severe European windstorms on annual timescales has substantial impacts on the (re-)insurance industry. Our knowledge of the risk is limited by large uncertainties in estimates of clustering from typical historical storm data sets covering the past few decades. Eight storm data sets are gathered for analysis in this study in order to reduce these uncertainties. Six of the data sets contain more than 100 years of severe storm information to reduce sampling errors, and observational errors are reduced by the diversity of information sources and analysis methods between storm data sets. All storm severity measures used in this study reflect damage, to suit (re-)insurance applications. The shortest storm data set of 42 years provides indications of stronger clustering with severity, particularly for regions off the main storm track in central Europe and France. However, clustering estimates have very large sampling and observational errors, exemplified by large changes in estimates in central Europe upon removal of one stormy season, 1989/1990. The extended storm records place 1989/1990 into a much longer historical context to produce more robust estimates of clustering. All the extended storm data sets show increased clustering between more severe storms from return periods (RPs) of 0.5 years to the longest measured RPs of about 20 years. Further, they contain signs of stronger clustering off the main storm track, and weaker clustering for smaller-sized areas, though these signals are more uncertain as they are drawn from smaller data samples. These new ultra-long storm data sets provide new information on clustering to improve our management of this risk.

Sampling procedures for throughfall monitoring: A simulation study

NASA Astrophysics Data System (ADS)

Zimmermann, Beate; Zimmermann, Alexander; Lark, Richard Murray; Elsenbeer, Helmut

2010-01-01

What is the most appropriate sampling scheme to estimate event-based average throughfall? A satisfactory answer to this seemingly simple question has yet to be found, a failure which we attribute to previous efforts' dependence on empirical studies. Here we try to answer this question by simulating stochastic throughfall fields based on parameters for statistical models of large monitoring data sets. We subsequently sampled these fields with different sampling designs and variable sample supports. We evaluated the performance of a particular sampling scheme with respect to the uncertainty of possible estimated means of throughfall volumes. Even for a relative error limit of 20%, an impractically large number of small, funnel-type collectors would be required to estimate mean throughfall, particularly for small events. While stratification of the target area is not superior to simple random sampling, cluster random sampling involves the risk of being less efficient. A larger sample support, e.g., the use of trough-type collectors, considerably reduces the necessary sample sizes and eliminates the sensitivity of the mean to outliers. Since the gain in time associated with the manual handling of troughs versus funnels depends on the local precipitation regime, the employment of automatically recording clusters of long troughs emerges as the most promising sampling scheme. Even so, a relative error of less than 5% appears out of reach for throughfall under heterogeneous canopies. We therefore suspect a considerable uncertainty of input parameters for interception models derived from measured throughfall, in particular, for those requiring data of small throughfall events.

Image Reconstruction for Interferometric Imaging of Geosynchronous Satellites

NASA Astrophysics Data System (ADS)

DeSantis, Zachary J.

Imaging distant objects at a high resolution has always presented a challenge due to the diffraction limit. Larger apertures improve the resolution, but at some point the cost of engineering, building, and correcting phase aberrations of large apertures become prohibitive. Interferometric imaging uses the Van Cittert-Zernike theorem to form an image from measurements of spatial coherence. This effectively allows the synthesis of a large aperture from two or more smaller telescopes to improve the resolution. We apply this method to imaging geosynchronous satellites with a ground-based system. Imaging a dim object from the ground presents unique challenges. The atmosphere creates errors in the phase measurements. The measurements are taken simultaneously across a large bandwidth of light. The atmospheric piston error, therefore, manifests as a linear phase error across the spectral measurements. Because the objects are faint, many of the measurements are expected to have a poor signal-to-noise ratio (SNR). This eliminates possibility of use of commonly used techniques like closure phase, which is a standard technique in astronomical interferometric imaging for making partial phase measurements in the presence of atmospheric error. The bulk of our work has been focused on forming an image, using sub-Nyquist sampled data, in the presence of these linear phase errors without relying on closure phase techniques. We present an image reconstruction algorithm that successfully forms an image in the presence of these linear phase errors. We demonstrate our algorithm’s success in both simulation and in laboratory experiments.

A simple, objective analysis scheme for scatterometer data. [Seasat A satellite observation of wind over ocean

NASA Technical Reports Server (NTRS)

Levy, G.; Brown, R. A.

1986-01-01

A simple economical objective analysis scheme is devised and tested on real scatterometer data. It is designed to treat dense data such as those of the Seasat A Satellite Scatterometer (SASS) for individual or multiple passes, and preserves subsynoptic scale features. Errors are evaluated with the aid of sampling ('bootstrap') statistical methods. In addition, sensitivity tests have been performed which establish qualitative confidence in calculated fields of divergence and vorticity. The SASS wind algorithm could be improved; however, the data at this point are limited by instrument errors rather than analysis errors. The analysis error is typically negligible in comparison with the instrument error, but amounts to 30 percent of the instrument error in areas of strong wind shear. The scheme is very economical, and thus suitable for large volumes of dense data such as SASS data.

High-frequency signal and noise estimates of CSR GRACE RL04

NASA Astrophysics Data System (ADS)

Bonin, Jennifer A.; Bettadpur, Srinivas; Tapley, Byron D.

2012-12-01

A sliding window technique is used to create daily-sampled Gravity Recovery and Climate Experiment (GRACE) solutions with the same background processing as the official CSR RL04 monthly series. By estimating over shorter time spans, more frequent solutions are made using uncorrelated data, allowing for higher frequency resolution in addition to daily sampling. Using these data sets, high-frequency GRACE errors are computed using two different techniques: assuming the GRACE high-frequency signal in a quiet area of the ocean is the true error, and computing the variance of differences between multiple high-frequency GRACE series from different centers. While the signal-to-noise ratios prove to be sufficiently high for confidence at annual and lower frequencies, at frequencies above 3 cycles/year the signal-to-noise ratios in the large hydrological basins looked at here are near 1.0. Comparisons with the GLDAS hydrological model and high frequency GRACE series developed at other centers confirm CSR GRACE RL04's poor ability to accurately and reliably measure hydrological signal above 3-9 cycles/year, due to the low power of the large-scale hydrological signal typical at those frequencies compared to the GRACE errors.

Statistical Analyses of Scatterplots to Identify Important Factors in Large-Scale Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kleijnen, J.P.C.; Helton, J.C.

1999-04-01

The robustness of procedures for identifying patterns in scatterplots generated in Monte Carlo sensitivity analyses is investigated. These procedures are based on attempts to detect increasingly complex patterns in the scatterplots under consideration and involve the identification of (1) linear relationships with correlation coefficients, (2) monotonic relationships with rank correlation coefficients, (3) trends in central tendency as defined by means, medians and the Kruskal-Wallis statistic, (4) trends in variability as defined by variances and interquartile ranges, and (5) deviations from randomness as defined by the chi-square statistic. The following two topics related to the robustness of these procedures are consideredmore » for a sequence of example analyses with a large model for two-phase fluid flow: the presence of Type I and Type II errors, and the stability of results obtained with independent Latin hypercube samples. Observations from analysis include: (1) Type I errors are unavoidable, (2) Type II errors can occur when inappropriate analysis procedures are used, (3) physical explanations should always be sought for why statistical procedures identify variables as being important, and (4) the identification of important variables tends to be stable for independent Latin hypercube samples.« less

SAChES: Scalable Adaptive Chain-Ensemble Sampling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Swiler, Laura Painton; Ray, Jaideep; Ebeida, Mohamed Salah

We present the development of a parallel Markov Chain Monte Carlo (MCMC) method called SAChES, Scalable Adaptive Chain-Ensemble Sampling. This capability is targed to Bayesian calibration of com- putationally expensive simulation models. SAChES involves a hybrid of two methods: Differential Evo- lution Monte Carlo followed by Adaptive Metropolis. Both methods involve parallel chains. Differential evolution allows one to explore high-dimensional parameter spaces using loosely coupled (i.e., largely asynchronous) chains. Loose coupling allows the use of large chain ensembles, with far more chains than the number of parameters to explore. This reduces per-chain sampling burden, enables high-dimensional inversions and the usemore » of computationally expensive forward models. The large number of chains can also ameliorate the impact of silent-errors, which may affect only a few chains. The chain ensemble can also be sampled to provide an initial condition when an aberrant chain is re-spawned. Adaptive Metropolis takes the best points from the differential evolution and efficiently hones in on the poste- rior density. The multitude of chains in SAChES is leveraged to (1) enable efficient exploration of the parameter space; and (2) ensure robustness to silent errors which may be unavoidable in extreme-scale computational platforms of the future. This report outlines SAChES, describes four papers that are the result of the project, and discusses some additional results.« less

Data processing 1: Advancements in machine analysis of multispectral data

NASA Technical Reports Server (NTRS)

Swain, P. H.

1972-01-01

Multispectral data processing procedures are outlined beginning with the data display process used to accomplish data editing and proceeding through clustering, feature selection criterion for error probability estimation, and sample clustering and sample classification. The effective utilization of large quantities of remote sensing data by formulating a three stage sampling model for evaluation of crop acreage estimates represents an improvement in determining the cost benefit relationship associated with remote sensing technology.

Hard choices in assessing survival past dams — a comparison of single- and paired-release strategies

USGS Publications Warehouse

Zydlewski, Joseph D.; Stich, Daniel S.; Sigourney, Douglas B.

2017-01-01

Mark–recapture models are widely used to estimate survival of salmon smolts migrating past dams. Paired releases have been used to improve estimate accuracy by removing components of mortality not attributable to the dam. This method is accompanied by reduced precision because (i) sample size is reduced relative to a single, large release; and (ii) variance calculations inflate error. We modeled an idealized system with a single dam to assess trade-offs between accuracy and precision and compared methods using root mean squared error (RMSE). Simulations were run under predefined conditions (dam mortality, background mortality, detection probability, and sample size) to determine scenarios when the paired release was preferable to a single release. We demonstrate that a paired-release design provides a theoretical advantage over a single-release design only at large sample sizes and high probabilities of detection. At release numbers typical of many survival studies, paired release can result in overestimation of dam survival. Failures to meet model assumptions of a paired release may result in further overestimation of dam-related survival. Under most conditions, a single-release strategy was preferable.

Parameter recovery, bias and standard errors in the linear ballistic accumulator model.

PubMed

Visser, Ingmar; Poessé, Rens

2017-05-01

The linear ballistic accumulator (LBA) model (Brown & Heathcote, , Cogn. Psychol., 57, 153) is increasingly popular in modelling response times from experimental data. An R package, glba, has been developed to fit the LBA model using maximum likelihood estimation which is validated by means of a parameter recovery study. At sufficient sample sizes parameter recovery is good, whereas at smaller sample sizes there can be large bias in parameters. In a second simulation study, two methods for computing parameter standard errors are compared. The Hessian-based method is found to be adequate and is (much) faster than the alternative bootstrap method. The use of parameter standard errors in model selection and inference is illustrated in an example using data from an implicit learning experiment (Visser et al., , Mem. Cogn., 35, 1502). It is shown that typical implicit learning effects are captured by different parameters of the LBA model. © 2017 The British Psychological Society.

Adjustment of regional regression models of urban-runoff quality using data for Chattanooga, Knoxville, and Nashville, Tennessee

USGS Publications Warehouse

Hoos, Anne B.; Patel, Anant R.

1996-01-01

Model-adjustment procedures were applied to the combined data bases of storm-runoff quality for Chattanooga, Knoxville, and Nashville, Tennessee, to improve predictive accuracy for storm-runoff quality for urban watersheds in these three cities and throughout Middle and East Tennessee. Data for 45 storms at 15 different sites (five sites in each city) constitute the data base. Comparison of observed values of storm-runoff load and event-mean concentration to the predicted values from the regional regression models for 10 constituents shows prediction errors, as large as 806,000 percent. Model-adjustment procedures, which combine the regional model predictions with local data, are applied to improve predictive accuracy. Standard error of estimate after model adjustment ranges from 67 to 322 percent. Calibration results may be biased due to sampling error in the Tennessee data base. The relatively large values of standard error of estimate for some of the constituent models, although representing significant reduction (at least 50 percent) in prediction error compared to estimation with unadjusted regional models, may be unacceptable for some applications. The user may wish to collect additional local data for these constituents and repeat the analysis, or calibrate an independent local regression model.

Evaluation of alternative model selection criteria in the analysis of unimodal response curves using CART

USGS Publications Warehouse

Ribic, C.A.; Miller, T.W.

1998-01-01

We investigated CART performance with a unimodal response curve for one continuous response and four continuous explanatory variables, where two variables were important (ie directly related to the response) and the other two were not. We explored performance under three relationship strengths and two explanatory variable conditions: equal importance and one variable four times as important as the other. We compared CART variable selection performance using three tree-selection rules ('minimum risk', 'minimum risk complexity', 'one standard error') to stepwise polynomial ordinary least squares (OLS) under four sample size conditions. The one-standard-error and minimum-risk-complexity methods performed about as well as stepwise OLS with large sample sizes when the relationship was strong. With weaker relationships, equally important explanatory variables and larger sample sizes, the one-standard-error and minimum-risk-complexity rules performed better than stepwise OLS. With weaker relationships and explanatory variables of unequal importance, tree-structured methods did not perform as well as stepwise OLS. Comparing performance within tree-structured methods, with a strong relationship and equally important explanatory variables, the one-standard-error-rule was more likely to choose the correct model than were the other tree-selection rules 1) with weaker relationships and equally important explanatory variables; and 2) under all relationship strengths when explanatory variables were of unequal importance and sample sizes were lower.

How Big of a Problem is Analytic Error in Secondary Analyses of Survey Data?

PubMed

West, Brady T; Sakshaug, Joseph W; Aurelien, Guy Alain S

2016-01-01

Secondary analyses of survey data collected from large probability samples of persons or establishments further scientific progress in many fields. The complex design features of these samples improve data collection efficiency, but also require analysts to account for these features when conducting analysis. Unfortunately, many secondary analysts from fields outside of statistics, biostatistics, and survey methodology do not have adequate training in this area, and as a result may apply incorrect statistical methods when analyzing these survey data sets. This in turn could lead to the publication of incorrect inferences based on the survey data that effectively negate the resources dedicated to these surveys. In this article, we build on the results of a preliminary meta-analysis of 100 peer-reviewed journal articles presenting analyses of data from a variety of national health surveys, which suggested that analytic errors may be extremely prevalent in these types of investigations. We first perform a meta-analysis of a stratified random sample of 145 additional research products analyzing survey data from the Scientists and Engineers Statistical Data System (SESTAT), which describes features of the U.S. Science and Engineering workforce, and examine trends in the prevalence of analytic error across the decades used to stratify the sample. We once again find that analytic errors appear to be quite prevalent in these studies. Next, we present several example analyses of real SESTAT data, and demonstrate that a failure to perform these analyses correctly can result in substantially biased estimates with standard errors that do not adequately reflect complex sample design features. Collectively, the results of this investigation suggest that reviewers of this type of research need to pay much closer attention to the analytic methods employed by researchers attempting to publish or present secondary analyses of survey data.

How Big of a Problem is Analytic Error in Secondary Analyses of Survey Data?

PubMed Central

West, Brady T.; Sakshaug, Joseph W.; Aurelien, Guy Alain S.

2016-01-01

Secondary analyses of survey data collected from large probability samples of persons or establishments further scientific progress in many fields. The complex design features of these samples improve data collection efficiency, but also require analysts to account for these features when conducting analysis. Unfortunately, many secondary analysts from fields outside of statistics, biostatistics, and survey methodology do not have adequate training in this area, and as a result may apply incorrect statistical methods when analyzing these survey data sets. This in turn could lead to the publication of incorrect inferences based on the survey data that effectively negate the resources dedicated to these surveys. In this article, we build on the results of a preliminary meta-analysis of 100 peer-reviewed journal articles presenting analyses of data from a variety of national health surveys, which suggested that analytic errors may be extremely prevalent in these types of investigations. We first perform a meta-analysis of a stratified random sample of 145 additional research products analyzing survey data from the Scientists and Engineers Statistical Data System (SESTAT), which describes features of the U.S. Science and Engineering workforce, and examine trends in the prevalence of analytic error across the decades used to stratify the sample. We once again find that analytic errors appear to be quite prevalent in these studies. Next, we present several example analyses of real SESTAT data, and demonstrate that a failure to perform these analyses correctly can result in substantially biased estimates with standard errors that do not adequately reflect complex sample design features. Collectively, the results of this investigation suggest that reviewers of this type of research need to pay much closer attention to the analytic methods employed by researchers attempting to publish or present secondary analyses of survey data. PMID:27355817

Sample-Clock Phase-Control Feedback

NASA Technical Reports Server (NTRS)

Quirk, Kevin J.; Gin, Jonathan W.; Nguyen, Danh H.; Nguyen, Huy

2012-01-01

To demodulate a communication signal, a receiver must recover and synchronize to the symbol timing of a received waveform. In a system that utilizes digital sampling, the fidelity of synchronization is limited by the time between the symbol boundary and closest sample time location. To reduce this error, one typically uses a sample clock in excess of the symbol rate in order to provide multiple samples per symbol, thereby lowering the error limit to a fraction of a symbol time. For systems with a large modulation bandwidth, the required sample clock rate is prohibitive due to current technological barriers and processing complexity. With precise control of the phase of the sample clock, one can sample the received signal at times arbitrarily close to the symbol boundary, thus obviating the need, from a synchronization perspective, for multiple samples per symbol. Sample-clock phase-control feedback was developed for use in the demodulation of an optical communication signal, where multi-GHz modulation bandwidths would require prohibitively large sample clock frequencies for rates in excess of the symbol rate. A custom mixedsignal (RF/digital) offset phase-locked loop circuit was developed to control the phase of the 6.4-GHz clock that samples the photon-counting detector output. The offset phase-locked loop is driven by a feedback mechanism that continuously corrects for variation in the symbol time due to motion between the transmitter and receiver as well as oscillator instability. This innovation will allow significant improvements in receiver throughput; for example, the throughput of a pulse-position modulation (PPM) with 16 slots can increase from 188 Mb/s to 1.5 Gb/s.

An audit strategy for time-to-event outcomes measured with error: application to five randomized controlled trials in oncology.

PubMed

Dodd, Lori E; Korn, Edward L; Freidlin, Boris; Gu, Wenjuan; Abrams, Jeffrey S; Bushnell, William D; Canetta, Renzo; Doroshow, James H; Gray, Robert J; Sridhara, Rajeshwari

2013-10-01

Measurement error in time-to-event end points complicates interpretation of treatment effects in clinical trials. Non-differential measurement error is unlikely to produce large bias [1]. When error depends on treatment arm, bias is of greater concern. Blinded-independent central review (BICR) of all images from a trial is commonly undertaken to mitigate differential measurement-error bias that may be present in hazard ratios (HRs) based on local evaluations. Similar BICR and local evaluation HRs may provide reassurance about the treatment effect, but BICR adds considerable time and expense to trials. We describe a BICR audit strategy [2] and apply it to five randomized controlled trials to evaluate its use and to provide practical guidelines. The strategy requires BICR on a subset of study subjects, rather than a complete-case BICR, and makes use of an auxiliary-variable estimator. When the effect size is relatively large, the method provides a substantial reduction in the size of the BICRs. In a trial with 722 participants and a HR of 0.48, an average audit of 28% of the data was needed and always confirmed the treatment effect as assessed by local evaluations. More moderate effect sizes and/or smaller trial sizes required larger proportions of audited images, ranging from 57% to 100% for HRs ranging from 0.55 to 0.77 and sample sizes between 209 and 737. The method is developed for a simple random sample of study subjects. In studies with low event rates, more efficient estimation may result from sampling individuals with events at a higher rate. The proposed strategy can greatly decrease the costs and time associated with BICR, by reducing the number of images undergoing review. The savings will depend on the underlying treatment effect and trial size, with larger treatment effects and larger trials requiring smaller proportions of audited data.

A Large Sample Procedure for Testing Coefficients of Ordinal Association: Goodman and Kruskal's Gamma and Somers' d ba and d ab

ERIC Educational Resources Information Center

Berry, Kenneth J.; And Others

1977-01-01

A FORTRAN program, GAMMA, computes Goodman and Kruskal's coefficient of ordinal association, gamma, and Somer's coefficient. The program also provides associated standard errors, standard scores, and probability values. (Author/JKS)

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

PubMed Central

Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

2015-01-01

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

North American vegetation model for land-use planning in a changing climate: A solution to large classification problems

Treesearch

Gerald E. Rehfeldt; Nicholas L. Crookston; Cuauhtemoc Saenz-Romero; Elizabeth M. Campbell

2012-01-01

Data points intensively sampling 46 North American biomes were used to predict the geographic distribution of biomes from climate variables using the Random Forests classification tree. Techniques were incorporated to accommodate a large number of classes and to predict the future occurrence of climates beyond the contemporary climatic range of the biomes. Errors of...

Multi-Reader ROC studies with Split-Plot Designs: A Comparison of Statistical Methods

PubMed Central

Obuchowski, Nancy A.; Gallas, Brandon D.; Hillis, Stephen L.

2012-01-01

Rationale and Objectives Multi-reader imaging trials often use a factorial design, where study patients undergo testing with all imaging modalities and readers interpret the results of all tests for all patients. A drawback of the design is the large number of interpretations required of each reader. Split-plot designs have been proposed as an alternative, in which one or a subset of readers interprets all images of a sample of patients, while other readers interpret the images of other samples of patients. In this paper we compare three methods of analysis for the split-plot design. Materials and Methods Three statistical methods are presented: Obuchowski-Rockette method modified for the split-plot design, a newly proposed marginal-mean ANOVA approach, and an extension of the three-sample U-statistic method. A simulation study using the Roe-Metz model was performed to compare the type I error rate, power and confidence interval coverage of the three test statistics. Results The type I error rates for all three methods are close to the nominal level but tend to be slightly conservative. The statistical power is nearly identical for the three methods. The coverage of 95% CIs fall close to the nominal coverage for small and large sample sizes. Conclusions The split-plot MRMC study design can be statistically efficient compared with the factorial design, reducing the number of interpretations required per reader. Three methods of analysis, shown to have nominal type I error rate, similar power, and nominal CI coverage, are available for this study design. PMID:23122570

Multi-reader ROC studies with split-plot designs: a comparison of statistical methods.

PubMed

Obuchowski, Nancy A; Gallas, Brandon D; Hillis, Stephen L

2012-12-01

Multireader imaging trials often use a factorial design, in which study patients undergo testing with all imaging modalities and readers interpret the results of all tests for all patients. A drawback of this design is the large number of interpretations required of each reader. Split-plot designs have been proposed as an alternative, in which one or a subset of readers interprets all images of a sample of patients, while other readers interpret the images of other samples of patients. In this paper, the authors compare three methods of analysis for the split-plot design. Three statistical methods are presented: the Obuchowski-Rockette method modified for the split-plot design, a newly proposed marginal-mean analysis-of-variance approach, and an extension of the three-sample U-statistic method. A simulation study using the Roe-Metz model was performed to compare the type I error rate, power, and confidence interval coverage of the three test statistics. The type I error rates for all three methods are close to the nominal level but tend to be slightly conservative. The statistical power is nearly identical for the three methods. The coverage of 95% confidence intervals falls close to the nominal coverage for small and large sample sizes. The split-plot multireader, multicase study design can be statistically efficient compared to the factorial design, reducing the number of interpretations required per reader. Three methods of analysis, shown to have nominal type I error rates, similar power, and nominal confidence interval coverage, are available for this study design. Copyright © 2012 AUR. All rights reserved.

Use of a control test to aid pH assessment of chemical eye injuries.

PubMed

Connor, A J; Severn, P

2009-11-01

Chemical burns of the eye represent 7.0%-9.9% of all ocular trauma. Initial management of ocular chemical injuries is irrigation of the eye and conjunctival sac until neutralisation of the tear surface pH is achieved.We present a case of alkali injury in which the raised tear film pH seemed to be unresponsive to irrigation treatment. Suspicion was raised about the accuracy of the litmus paper used to test the tear film pH. The error was confirmed by use of a control litmus pH test of the examining doctor's eyes. Errors in litmus paper pH measurement can occur because of difficulty in matching the paper with scale colours and drying of the paper, which produces a darker colour. A small tear film sample can also create difficulty in colour matching, whereas too large a sample can wash away pigment from the litmus paper. Samples measured too quickly after irrigation can result in a falsely neutral pH measurement. Use of faulty or inappropriate materials can also result in errors. We advocate the use of control litmus pH test in all patients. This would highlight errors in pH measurements and aid in the detection of the end point of irrigation.

Certification of ICI 1012 optical data storage tape

NASA Technical Reports Server (NTRS)

Howell, J. M.

1993-01-01

ICI has developed a unique and novel method of certifying a Terabyte optical tape. The tape quality is guaranteed as a statistical upper limit on the probability of uncorrectable errors. This is called the Corrected Byte Error Rate or CBER. We developed this probabilistic method because of two reasons why error rate cannot be measured directly. Firstly, written data is indelible, so one cannot employ write/read tests such as used for magnetic tape. Secondly, the anticipated error rates need impractically large samples to measure accurately. For example, a rate of 1E-12 implies only one byte in error per tape. The archivability of ICI 1012 Data Storage Tape in general is well characterized and understood. Nevertheless, customers expect performance guarantees to be supported by test results on individual tapes. In particular, they need assurance that data is retrievable after decades in archive. This paper describes the mathematical basis, measurement apparatus and applicability of the certification method.

Temporally-stable active precision mount for large optics.

PubMed

Reinlein, Claudia; Damm, Christoph; Lange, Nicolas; Kamm, Andreas; Mohaupt, Matthias; Brady, Aoife; Goy, Matthias; Leonhard, Nina; Eberhardt, Ramona; Zeitner, Uwe; Tünnermann, Andreas

2016-06-13

We present a temporally-stable active mount to compensate for manufacturing-induced deformations of reflective optical components. In this paper, we introduce the design of the active mount, and its evaluation results for two sample mirrors: a quarter mirror of 115 × 105 × 9 mm³, and a full mirror of 228 × 210 × 9 mm³. The quarter mirror with 20 actuators shows a best wavefront error rms of 10 nm. Its installation position depending deformations are addressed by long-time measurements over 14 weeks indicating no significance of the orientation. Size-induced differences of the mount are studied by a full mirror with 80 manual actuators arranged in the same actuator pattern as the quarter mirror. This sample shows a wavefront error rms of (27±2) nm over a measurement period of 46 days. We conclude that the developed mount is suitable to compensate for manufacturing-induced deformations of large reflective optics, and likely to be included in the overall systems alignment procedure.

SU-E-T-769: T-Test Based Prior Error Estimate and Stopping Criterion for Monte Carlo Dose Calculation in Proton Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hong, X; Gao, H; Schuemann, J

2015-06-15

Purpose: The Monte Carlo (MC) method is a gold standard for dose calculation in radiotherapy. However, it is not a priori clear how many particles need to be simulated to achieve a given dose accuracy. Prior error estimate and stopping criterion are not well established for MC. This work aims to fill this gap. Methods: Due to the statistical nature of MC, our approach is based on one-sample t-test. We design the prior error estimate method based on the t-test, and then use this t-test based error estimate for developing a simulation stopping criterion. The three major components are asmore » follows.First, the source particles are randomized in energy, space and angle, so that the dose deposition from a particle to the voxel is independent and identically distributed (i.i.d.).Second, a sample under consideration in the t-test is the mean value of dose deposition to the voxel by sufficiently large number of source particles. Then according to central limit theorem, the sample as the mean value of i.i.d. variables is normally distributed with the expectation equal to the true deposited dose.Third, the t-test is performed with the null hypothesis that the difference between sample expectation (the same as true deposited dose) and on-the-fly calculated mean sample dose from MC is larger than a given error threshold, in addition to which users have the freedom to specify confidence probability and region of interest in the t-test based stopping criterion. Results: The method is validated for proton dose calculation. The difference between the MC Result based on the t-test prior error estimate and the statistical Result by repeating numerous MC simulations is within 1%. Conclusion: The t-test based prior error estimate and stopping criterion are developed for MC and validated for proton dose calculation. Xiang Hong and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)« less

A comparison of methods for deriving solute flux rates using long-term data from streams in the mirror lake watershed

USGS Publications Warehouse

Bukaveckas, P.A.; Likens, G.E.; Winter, T.C.; Buso, D.C.

1998-01-01

Calculation of chemical flux rates for streams requires integration of continuous measurements of discharge with discrete measurements of solute concentrations. We compared two commonly used methods for interpolating chemistry data (time-averaging and flow-weighting) to determine whether discrepancies between the two methods were large relative to other sources of error in estimating flux rates. Flux rates of dissolved Si and SO42- were calculated from 10 years of data (1981-1990) for the NW inlet and Outlet of Mirror Lake and for a 40-day period (March 22 to April 30, 1993) during which we augmented our routine (weekly) chemical monitoring with collection of daily samples. The time-averaging method yielded higher estimates of solute flux during high-flow periods if no chemistry samples were collected corresponding to peak discharge. Concentration-discharge relationships should be used to interpolate stream chemistry during changing flow conditions if chemical changes are large. Caution should be used in choosing the appropriate time-scale over which data are pooled to derive the concentration-discharge regressions because the model parameters (slope and intercept) were found to be sensitive to seasonal and inter-annual variation. Both methods approximated solute flux to within 2-10% for a range of solutes that were monitored during the intensive sampling period. Our results suggest that errors arising from interpolation of stream chemistry data are small compared with other sources of error in developing watershed mass balances.

Evaluating the accuracy of sampling to estimate central line-days: simplification of the National Healthcare Safety Network surveillance methods.

PubMed

Thompson, Nicola D; Edwards, Jonathan R; Bamberg, Wendy; Beldavs, Zintars G; Dumyati, Ghinwa; Godine, Deborah; Maloney, Meghan; Kainer, Marion; Ray, Susan; Thompson, Deborah; Wilson, Lucy; Magill, Shelley S

2013-03-01

To evaluate the accuracy of weekly sampling of central line-associated bloodstream infection (CLABSI) denominator data to estimate central line-days (CLDs). Obtained CLABSI denominator logs showing daily counts of patient-days and CLD for 6-12 consecutive months from participants and CLABSI numerators and facility and location characteristics from the National Healthcare Safety Network (NHSN). Convenience sample of 119 inpatient locations in 63 acute care facilities within 9 states participating in the Emerging Infections Program. Actual CLD and estimated CLD obtained from sampling denominator data on all single-day and 2-day (day-pair) samples were compared by assessing the distributions of the CLD percentage error. Facility and location characteristics associated with increased precision of estimated CLD were assessed. The impact of using estimated CLD to calculate CLABSI rates was evaluated by measuring the change in CLABSI decile ranking. The distribution of CLD percentage error varied by the day and number of days sampled. On average, day-pair samples provided more accurate estimates than did single-day samples. For several day-pair samples, approximately 90% of locations had CLD percentage error of less than or equal to ±5%. A lower number of CLD per month was most significantly associated with poor precision in estimated CLD. Most locations experienced no change in CLABSI decile ranking, and no location's CLABSI ranking changed by more than 2 deciles. Sampling to obtain estimated CLD is a valid alternative to daily data collection for a large proportion of locations. Development of a sampling guideline for NHSN users is underway.

Errors in patient specimen collection: application of statistical process control.

PubMed

Dzik, Walter Sunny; Beckman, Neil; Selleng, Kathleen; Heddle, Nancy; Szczepiorkowski, Zbigniew; Wendel, Silvano; Murphy, Michael

2008-10-01

Errors in the collection and labeling of blood samples for pretransfusion testing increase the risk of transfusion-associated patient morbidity and mortality. Statistical process control (SPC) is a recognized method to monitor the performance of a critical process. An easy-to-use SPC method was tested to determine its feasibility as a tool for monitoring quality in transfusion medicine. SPC control charts were adapted to a spreadsheet presentation. Data tabulating the frequency of mislabeled and miscollected blood samples from 10 hospitals in five countries from 2004 to 2006 were used to demonstrate the method. Control charts were produced to monitor process stability. The participating hospitals found the SPC spreadsheet very suitable to monitor the performance of the sample labeling and collection and applied SPC charts to suit their specific needs. One hospital monitored subcategories of sample error in detail. A large hospital monitored the number of wrong-blood-in-tube (WBIT) events. Four smaller-sized facilities, each following the same policy for sample collection, combined their data on WBIT samples into a single control chart. One hospital used the control chart to monitor the effect of an educational intervention. A simple SPC method is described that can monitor the process of sample collection and labeling in any hospital. SPC could be applied to other critical steps in the transfusion processes as a tool for biovigilance and could be used to develop regional or national performance standards for pretransfusion sample collection. A link is provided to download the spreadsheet for free.

Detection of the pairwise kinematic Sunyaev-Zel'dovich effect with BOSS DR11 and the Atacama Cosmology Telescope

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernardis, F. De; Aiola, S.; Vavagiakis, E. M.

Here, we present a new measurement of the kinematic Sunyaev-Zel'dovich effect using data from the Atacama Cosmology Telescope (ACT) and the Baryon Oscillation Spectroscopic Survey (BOSS). Using 600 square degrees of overlapping sky area, we evaluate the mean pairwise baryon momentum associated with the positions of 50,000 bright galaxies in the BOSS DR11 Large Scale Structure catalog. A non-zero signal arises from the large-scale motions of halos containing the sample galaxies. The data fits an analytical signal model well, with the optical depth to microwave photon scattering as a free parameter determining the overall signal amplitude. We estimate the covariancemore » matrix of the mean pairwise momentum as a function of galaxy separation, using microwave sky simulations, jackknife evaluation, and bootstrap estimates. The most conservative simulation-based errors give signal-to-noise estimates between 3.6 and 4.1 for varying galaxy luminosity cuts. We discuss how the other error determinations can lead to higher signal-to-noise values, and consider the impact of several possible systematic errors. Estimates of the optical depth from the average thermal Sunyaev-Zel'dovich signal at the sample galaxy positions are broadly consistent with those obtained from the mean pairwise momentum signal.« less

Detection of the pairwise kinematic Sunyaev-Zel'dovich effect with BOSS DR11 and the Atacama Cosmology Telescope

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernardis, F. De; Vavagiakis, E.M.; Niemack, M.D.

We present a new measurement of the kinematic Sunyaev-Zel'dovich effect using data from the Atacama Cosmology Telescope (ACT) and the Baryon Oscillation Spectroscopic Survey (BOSS). Using 600 square degrees of overlapping sky area, we evaluate the mean pairwise baryon momentum associated with the positions of 50,000 bright galaxies in the BOSS DR11 Large Scale Structure catalog. A non-zero signal arises from the large-scale motions of halos containing the sample galaxies. The data fits an analytical signal model well, with the optical depth to microwave photon scattering as a free parameter determining the overall signal amplitude. We estimate the covariance matrixmore » of the mean pairwise momentum as a function of galaxy separation, using microwave sky simulations, jackknife evaluation, and bootstrap estimates. The most conservative simulation-based errors give signal-to-noise estimates between 3.6 and 4.1 for varying galaxy luminosity cuts. We discuss how the other error determinations can lead to higher signal-to-noise values, and consider the impact of several possible systematic errors. Estimates of the optical depth from the average thermal Sunyaev-Zel'dovich signal at the sample galaxy positions are broadly consistent with those obtained from the mean pairwise momentum signal.« less

Detection of the Pairwise Kinematic Sunyaev-Zel'dovich Effect with BOSS DR11 and the Atacama Cosmology Telescope

NASA Technical Reports Server (NTRS)

De Bernardis, F.; Aiola, S.; Vavagiakis, E. M.; Battaglia, N.; Niemack, M. D.; Beall, J.; Becker, D. T.; Bond, J. R.; Calabrese, E.; Cho, H.;

Detection of the pairwise kinematic Sunyaev-Zel'dovich effect with BOSS DR11 and the Atacama Cosmology Telescope

NASA Astrophysics Data System (ADS)

De Bernardis, F.; Aiola, S.; Vavagiakis, E. M.; Battaglia, N.; Niemack, M. D.; Beall, J.; Becker, D. T.; Bond, J. R.; Calabrese, E.; Cho, H.; Coughlin, K.; Datta, R.; Devlin, M.; Dunkley, J.; Dunner, R.; Ferraro, S.; Fox, A.; Gallardo, P. A.; Halpern, M.; Hand, N.; Hasselfield, M.; Henderson, S. W.; Hill, J. C.; Hilton, G. C.; Hilton, M.; Hincks, A. D.; Hlozek, R.; Hubmayr, J.; Huffenberger, K.; Hughes, J. P.; Irwin, K. D.; Koopman, B. J.; Kosowsky, A.; Li, D.; Louis, T.; Lungu, M.; Madhavacheril, M. S.; Maurin, L.; McMahon, J.; Moodley, K.; Naess, S.; Nati, F.; Newburgh, L.; Nibarger, J. P.; Page, L. A.; Partridge, B.; Schaan, E.; Schmitt, B. L.; Sehgal, N.; Sievers, J.; Simon, S. M.; Spergel, D. N.; Staggs, S. T.; Stevens, J. R.; Thornton, R. J.; van Engelen, A.; Van Lanen, J.; Wollack, E. J.

2017-03-01

We present a new measurement of the kinematic Sunyaev-Zel'dovich effect using data from the Atacama Cosmology Telescope (ACT) and the Baryon Oscillation Spectroscopic Survey (BOSS). Using 600 square degrees of overlapping sky area, we evaluate the mean pairwise baryon momentum associated with the positions of 50,000 bright galaxies in the BOSS DR11 Large Scale Structure catalog. A non-zero signal arises from the large-scale motions of halos containing the sample galaxies. The data fits an analytical signal model well, with the optical depth to microwave photon scattering as a free parameter determining the overall signal amplitude. We estimate the covariance matrix of the mean pairwise momentum as a function of galaxy separation, using microwave sky simulations, jackknife evaluation, and bootstrap estimates. The most conservative simulation-based errors give signal-to-noise estimates between 3.6 and 4.1 for varying galaxy luminosity cuts. We discuss how the other error determinations can lead to higher signal-to-noise values, and consider the impact of several possible systematic errors. Estimates of the optical depth from the average thermal Sunyaev-Zel'dovich signal at the sample galaxy positions are broadly consistent with those obtained from the mean pairwise momentum signal.

Detection of the pairwise kinematic Sunyaev-Zel'dovich effect with BOSS DR11 and the Atacama Cosmology Telescope

DOE PAGES

Bernardis, F. De; Aiola, S.; Vavagiakis, E. M.; ...

2017-03-07

Here, we present a new measurement of the kinematic Sunyaev-Zel'dovich effect using data from the Atacama Cosmology Telescope (ACT) and the Baryon Oscillation Spectroscopic Survey (BOSS). Using 600 square degrees of overlapping sky area, we evaluate the mean pairwise baryon momentum associated with the positions of 50,000 bright galaxies in the BOSS DR11 Large Scale Structure catalog. A non-zero signal arises from the large-scale motions of halos containing the sample galaxies. The data fits an analytical signal model well, with the optical depth to microwave photon scattering as a free parameter determining the overall signal amplitude. We estimate the covariancemore » matrix of the mean pairwise momentum as a function of galaxy separation, using microwave sky simulations, jackknife evaluation, and bootstrap estimates. The most conservative simulation-based errors give signal-to-noise estimates between 3.6 and 4.1 for varying galaxy luminosity cuts. We discuss how the other error determinations can lead to higher signal-to-noise values, and consider the impact of several possible systematic errors. Estimates of the optical depth from the average thermal Sunyaev-Zel'dovich signal at the sample galaxy positions are broadly consistent with those obtained from the mean pairwise momentum signal.« less

Heuristic-driven graph wavelet modeling of complex terrain

NASA Astrophysics Data System (ADS)

Cioacǎ, Teodor; Dumitrescu, Bogdan; Stupariu, Mihai-Sorin; Pǎtru-Stupariu, Ileana; Nǎpǎrus, Magdalena; Stoicescu, Ioana; Peringer, Alexander; Buttler, Alexandre; Golay, François

2015-03-01

We present a novel method for building a multi-resolution representation of large digital surface models. The surface points coincide with the nodes of a planar graph which can be processed using a critically sampled, invertible lifting scheme. To drive the lazy wavelet node partitioning, we employ an attribute aware cost function based on the generalized quadric error metric. The resulting algorithm can be applied to multivariate data by storing additional attributes at the graph's nodes. We discuss how the cost computation mechanism can be coupled with the lifting scheme and examine the results by evaluating the root mean square error. The algorithm is experimentally tested using two multivariate LiDAR sets representing terrain surface and vegetation structure with different sampling densities.

Effects of model error on control of large flexible space antenna with comparisons of decoupled and linear quadratic regulator control procedures

NASA Technical Reports Server (NTRS)

Hamer, H. A.; Johnson, K. G.

1986-01-01

An analysis was performed to determine the effects of model error on the control of a large flexible space antenna. Control was achieved by employing two three-axis control-moment gyros (CMG's) located on the antenna column. State variables were estimated by including an observer in the control loop that used attitude and attitude-rate sensors on the column. Errors were assumed to exist in the individual model parameters: modal frequency, modal damping, mode slope (control-influence coefficients), and moment of inertia. Their effects on control-system performance were analyzed either for (1) nulling initial disturbances in the rigid-body modes, or (2) nulling initial disturbances in the first three flexible modes. The study includes the effects on stability, time to null, and control requirements (defined as maximum torque and total momentum), as well as on the accuracy of obtaining initial estimates of the disturbances. The effects on the transients of the undisturbed modes are also included. The results, which are compared for decoupled and linear quadratic regulator (LQR) control procedures, are shown in tabular form, parametric plots, and as sample time histories of modal-amplitude and control responses. Results of the analysis showed that the effects of model errors on the control-system performance were generally comparable for both control procedures. The effect of mode-slope error was the most serious of all model errors.

Data Combination and Instrumental Variables in Linear Models

ERIC Educational Resources Information Center

Khawand, Christopher

2012-01-01

Instrumental variables (IV) methods allow for consistent estimation of causal effects, but suffer from poor finite-sample properties and data availability constraints. IV estimates also tend to have relatively large standard errors, often inhibiting the interpretability of differences between IV and non-IV point estimates. Lastly, instrumental…

Selecting SNPs informative for African, American Indian and European Ancestry: application to the Family Investigation of Nephropathy and Diabetes (FIND).

PubMed

Williams, Robert C; Elston, Robert C; Kumar, Pankaj; Knowler, William C; Abboud, Hanna E; Adler, Sharon; Bowden, Donald W; Divers, Jasmin; Freedman, Barry I; Igo, Robert P; Ipp, Eli; Iyengar, Sudha K; Kimmel, Paul L; Klag, Michael J; Kohn, Orly; Langefeld, Carl D; Leehey, David J; Nelson, Robert G; Nicholas, Susanne B; Pahl, Madeleine V; Parekh, Rulan S; Rotter, Jerome I; Schelling, Jeffrey R; Sedor, John R; Shah, Vallabh O; Smith, Michael W; Taylor, Kent D; Thameem, Farook; Thornley-Brown, Denyse; Winkler, Cheryl A; Guo, Xiuqing; Zager, Phillip; Hanson, Robert L

2016-05-04

The presence of population structure in a sample may confound the search for important genetic loci associated with disease. Our four samples in the Family Investigation of Nephropathy and Diabetes (FIND), European Americans, Mexican Americans, African Americans, and American Indians are part of a genome- wide association study in which population structure might be particularly important. We therefore decided to study in detail one component of this, individual genetic ancestry (IGA). From SNPs present on the Affymetrix 6.0 Human SNP array, we identified 3 sets of ancestry informative markers (AIMs), each maximized for the information in one the three contrasts among ancestral populations: Europeans (HAPMAP, CEU), Africans (HAPMAP, YRI and LWK), and Native Americans (full heritage Pima Indians). We estimate IGA and present an algorithm for their standard errors, compare IGA to principal components, emphasize the importance of balancing information in the ancestry informative markers (AIMs), and test the association of IGA with diabetic nephropathy in the combined sample. A fixed parental allele maximum likelihood algorithm was applied to the FIND to estimate IGA in four samples: 869 American Indians; 1385 African Americans; 1451 Mexican Americans; and 826 European Americans. When the information in the AIMs is unbalanced, the estimates are incorrect with large error. Individual genetic admixture is highly correlated with principle components for capturing population structure. It takes ~700 SNPs to reduce the average standard error of individual admixture below 0.01. When the samples are combined, the resulting population structure creates associations between IGA and diabetic nephropathy. The identified set of AIMs, which include American Indian parental allele frequencies, may be particularly useful for estimating genetic admixture in populations from the Americas. Failure to balance information in maximum likelihood, poly-ancestry models creates biased estimates of individual admixture with large error. This also occurs when estimating IGA using the Bayesian clustering method as implemented in the program STRUCTURE. Odds ratios for the associations of IGA with disease are consistent with what is known about the incidence and prevalence of diabetic nephropathy in these populations.

Changes in Rod and Frame Test Scores Recorded in Schoolchildren during Development – A Longitudinal Study

PubMed Central

Bagust, Jeff; Docherty, Sharon; Haynes, Wayne; Telford, Richard; Isableu, Brice

2013-01-01

The Rod and Frame Test has been used to assess the degree to which subjects rely on the visual frame of reference to perceive vertical (visual field dependence- independence perceptual style). Early investigations found children exhibited a wide range of alignment errors, which reduced as they matured. These studies used a mechanical Rod and Frame system, and presented only mean values of grouped data. The current study also considered changes in individual performance. Changes in rod alignment accuracy in 419 school children were measured using a computer-based Rod and Frame test. Each child was tested at school Grade 2 and retested in Grades 4 and 6. The results confirmed that children displayed a wide range of alignment errors, which decreased with age but did not reach the expected adult values. Although most children showed a decrease in frame dependency over the 4 years of the study, almost 20% had increased alignment errors suggesting that they were becoming more frame-dependent. Plots of individual variation (SD) against mean error allowed the sample to be divided into 4 groups; the majority with small errors and SDs; a group with small SDs, but alignments clustering around the frame angle of 18°; a group showing large errors in the opposite direction to the frame tilt; and a small number with large SDs whose alignment appeared to be random. The errors in the last 3 groups could largely be explained by alignment of the rod to different aspects of the frame. At corresponding ages females exhibited larger alignment errors than males although this did not reach statistical significance. This study confirms that children rely more heavily on the visual frame of reference for processing spatial orientation cues. Most become less frame-dependent as they mature, but there are considerable individual differences. PMID:23724139

Reliable estimation of orbit errors in spaceborne SAR interferometry. The network approach

NASA Astrophysics Data System (ADS)

Bähr, Hermann; Hanssen, Ramon F.

2012-12-01

An approach to improve orbital state vectors by orbit error estimates derived from residual phase patterns in synthetic aperture radar interferograms is presented. For individual interferograms, an error representation by two parameters is motivated: the baseline error in cross-range and the rate of change of the baseline error in range. For their estimation, two alternatives are proposed: a least squares approach that requires prior unwrapping and a less reliable gridsearch method handling the wrapped phase. In both cases, reliability is enhanced by mutual control of error estimates in an overdetermined network of linearly dependent interferometric combinations of images. Thus, systematic biases, e.g., due to unwrapping errors, can be detected and iteratively eliminated. Regularising the solution by a minimum-norm condition results in quasi-absolute orbit errors that refer to particular images. For the 31 images of a sample ENVISAT dataset, orbit corrections with a mutual consistency on the millimetre level have been inferred from 163 interferograms. The method itself qualifies by reliability and rigorous geometric modelling of the orbital error signal but does not consider interfering large scale deformation effects. However, a separation may be feasible in a combined processing with persistent scatterer approaches or by temporal filtering of the estimates.

LACIE performance predictor final operational capability program description, volume 3

NASA Technical Reports Server (NTRS)

1976-01-01

The requirements and processing logic for the LACIE Error Model program (LEM) are described. This program is an integral part of the Large Area Crop Inventory Experiment (LACIE) system. LEM is that portion of the LPP (LACIE Performance Predictor) which simulates the sample segment classification, strata yield estimation, and production aggregation. LEM controls repetitive Monte Carlo trials based on input error distributions to obtain statistical estimates of the wheat area, yield, and production at different levels of aggregation. LEM interfaces with the rest of the LPP through a set of data files.

Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error.

PubMed

Lu, Xinjiang; Liu, Wenbo; Zhou, Chuang; Huang, Minghui

2017-06-13

The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers. In this paper, a robust LS-SVM method is proposed and is shown to have more reliable performance when modeling a nonlinear system under conditions where Gaussian or non-Gaussian noise is present. The construction of a new objective function allows for a reduction of the mean of the modeling error as well as the minimization of its variance, and it does not constrain the mean of the modeling error to zero. This differs from the traditional LS-SVM, which uses a worst-case scenario approach in order to minimize the modeling error and constrains the mean of the modeling error to zero. In doing so, the proposed method takes the modeling error distribution information into consideration and is thus less conservative and more robust in regards to random noise. A solving method is then developed in order to determine the optimal parameters for the proposed robust LS-SVM. An additional analysis indicates that the proposed LS-SVM gives a smaller weight to a large-error training sample and a larger weight to a small-error training sample, and is thus more robust than the traditional LS-SVM. The effectiveness of the proposed robust LS-SVM is demonstrated using both artificial and real life cases.

An integrated approach to piezoactuator positioning in high-speed atomic force microscope imaging

NASA Astrophysics Data System (ADS)

Yan, Yan; Wu, Ying; Zou, Qingze; Su, Chanmin

2008-07-01

In this paper, an integrated approach to achieve high-speed atomic force microscope (AFM) imaging of large-size samples is proposed, which combines the enhanced inversion-based iterative control technique to drive the piezotube actuator control for lateral x-y axis positioning with the use of a dual-stage piezoactuator for vertical z-axis positioning. High-speed, large-size AFM imaging is challenging because in high-speed lateral scanning of the AFM imaging at large size, large positioning error of the AFM probe relative to the sample can be generated due to the adverse effects—the nonlinear hysteresis and the vibrational dynamics of the piezotube actuator. In addition, vertical precision positioning of the AFM probe is even more challenging (than the lateral scanning) because the desired trajectory (i.e., the sample topography profile) is unknown in general, and the probe positioning is also effected by and sensitive to the probe-sample interaction. The main contribution of this article is the development of an integrated approach that combines advanced control algorithm with an advanced hardware platform. The proposed approach is demonstrated in experiments by imaging a large-size (50μm ) calibration sample at high-speed (50Hz scan rate).

Uncertainty in predicting soil hydraulic properties at the hillslope scale with indirect methods

NASA Astrophysics Data System (ADS)

Chirico, G. B.; Medina, H.; Romano, N.

2007-02-01

SummarySeveral hydrological applications require the characterisation of the soil hydraulic properties at large spatial scales. Pedotransfer functions (PTFs) are being developed as simplified methods to estimate soil hydraulic properties as an alternative to direct measurements, which are unfeasible for most practical circumstances. The objective of this study is to quantify the uncertainty in PTFs spatial predictions at the hillslope scale as related to the sampling density, due to: (i) the error in estimated soil physico-chemical properties and (ii) PTF model error. The analysis is carried out on a 2-km-long experimental hillslope in South Italy. The method adopted is based on a stochastic generation of patterns of soil variables using sequential Gaussian simulation, conditioned to the observed sample data. The following PTFs are applied: Vereecken's PTF [Vereecken, H., Diels, J., van Orshoven, J., Feyen, J., Bouma, J., 1992. Functional evaluation of pedotransfer functions for the estimation of soil hydraulic properties. Soil Sci. Soc. Am. J. 56, 1371-1378] and HYPRES PTF [Wösten, J.H.M., Lilly, A., Nemes, A., Le Bas, C., 1999. Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169-185]. The two PTFs estimate reliably the soil water retention characteristic even for a relatively coarse sampling resolution, with prediction uncertainties comparable to the uncertainties in direct laboratory or field measurements. The uncertainty of soil water retention prediction due to the model error is as much as or more significant than the uncertainty associated with the estimated input, even for a relatively coarse sampling resolution. Prediction uncertainties are much more important when PTF are applied to estimate the saturated hydraulic conductivity. In this case model error dominates the overall prediction uncertainties, making negligible the effect of the input error.

Field evaluation of the error arising from inadequate time averaging in the standard use of depth-integrating suspended-sediment samplers

USGS Publications Warehouse

Topping, David J.; Rubin, David M.; Wright, Scott A.; Melis, Theodore S.

2011-01-01

Several common methods for measuring suspended-sediment concentration in rivers in the United States use depth-integrating samplers to collect a velocity-weighted suspended-sediment sample in a subsample of a river cross section. Because depth-integrating samplers are always moving through the water column as they collect a sample, and can collect only a limited volume of water and suspended sediment, they collect only minimally time-averaged data. Four sources of error exist in the field use of these samplers: (1) bed contamination, (2) pressure-driven inrush, (3) inadequate sampling of the cross-stream spatial structure in suspended-sediment concentration, and (4) inadequate time averaging. The first two of these errors arise from misuse of suspended-sediment samplers, and the third has been the subject of previous study using data collected in the sand-bedded Middle Loup River in Nebraska. Of these four sources of error, the least understood source of error arises from the fact that depth-integrating samplers collect only minimally time-averaged data. To evaluate this fourth source of error, we collected suspended-sediment data between 1995 and 2007 at four sites on the Colorado River in Utah and Arizona, using a P-61 suspended-sediment sampler deployed in both point- and one-way depth-integrating modes, and D-96-A1 and D-77 bag-type depth-integrating suspended-sediment samplers. These data indicate that the minimal duration of time averaging during standard field operation of depth-integrating samplers leads to an error that is comparable in magnitude to that arising from inadequate sampling of the cross-stream spatial structure in suspended-sediment concentration. This random error arising from inadequate time averaging is positively correlated with grain size and does not largely depend on flow conditions or, for a given size class of suspended sediment, on elevation above the bed. Averaging over time scales >1 minute is the likely minimum duration required to result in substantial decreases in this error. During standard two-way depth integration, a depth-integrating suspended-sediment sampler collects a sample of the water-sediment mixture during two transits at each vertical in a cross section: one transit while moving from the water surface to the bed, and another transit while moving from the bed to the water surface. As the number of transits is doubled at an individual vertical, this error is reduced by ~30 percent in each size class of suspended sediment. For a given size class of suspended sediment, the error arising from inadequate sampling of the cross-stream spatial structure in suspended-sediment concentration depends only on the number of verticals collected, whereas the error arising from inadequate time averaging depends on both the number of verticals collected and the number of transits collected at each vertical. Summing these two errors in quadrature yields a total uncertainty in an equal-discharge-increment (EDI) or equal-width-increment (EWI) measurement of the time-averaged velocity-weighted suspended-sediment concentration in a river cross section (exclusive of any laboratory-processing errors). By virtue of how the number of verticals and transits influences the two individual errors within this total uncertainty, the error arising from inadequate time averaging slightly dominates that arising from inadequate sampling of the cross-stream spatial structure in suspended-sediment concentration. Adding verticals to an EDI or EWI measurement is slightly more effective in reducing the total uncertainty than adding transits only at each vertical, because a new vertical contributes both temporal and spatial information. However, because collection of depth-integrated samples at more transits at each vertical is generally easier and faster than at more verticals, addition of a combination of verticals and transits is likely a more practical approach to reducing the total uncertainty in most field situatio

Statistically Controlling for Confounding Constructs Is Harder than You Think

PubMed Central

Westfall, Jacob; Yarkoni, Tal

2016-01-01

Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest—in some cases approaching 100%—when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by reviewing SEM-based statistical approaches that appropriately control the Type I error rate when attempting to establish incremental validity. PMID:27031707

Factors affecting nursing students' intention to report medication errors: An application of the theory of planned behavior.

PubMed

Ben Natan, Merav; Sharon, Ira; Mahajna, Marlen; Mahajna, Sara

2017-11-01

Medication errors are common among nursing students. Nonetheless, these errors are often underreported. To examine factors related to nursing students' intention to report medication errors, using the Theory of Planned Behavior, and to examine whether the theory is useful in predicting students' intention to report errors. This study has a descriptive cross-sectional design. Study population was recruited in a university and a large nursing school in central and northern Israel. A convenience sample of 250 nursing students took part in the study. The students completed a self-report questionnaire, based on the Theory of Planned Behavior. The findings indicate that students' intention to report medication errors was high. The Theory of Planned Behavior constructs explained 38% of variance in students' intention to report medication errors. The constructs of behavioral beliefs, subjective norms, and perceived behavioral control were found as affecting this intention, while the most significant factor was behavioral beliefs. The findings also reveal that students' fear of the reaction to disclosure of the error from superiors and colleagues may impede them from reporting the error. Understanding factors related to reporting medication errors is crucial to designing interventions that foster error reporting. Copyright © 2017 Elsevier Ltd. All rights reserved.

Performance of the likelihood ratio difference (G2 Diff) test for detecting unidimensionality in applications of the multidimensional Rasch model.

PubMed

Harrell-Williams, Leigh; Wolfe, Edward W

2014-01-01

Previous research has investigated the influence of sample size, model misspecification, test length, ability distribution offset, and generating model on the likelihood ratio difference test in applications of item response models. This study extended that research to the evaluation of dimensionality using the multidimensional random coefficients multinomial logit model (MRCMLM). Logistic regression analysis of simulated data reveal that sample size and test length have a large effect on the capacity of the LR difference test to correctly identify unidimensionality, with shorter tests and smaller sample sizes leading to smaller Type I error rates. Higher levels of simulated misfit resulted in fewer incorrect decisions than data with no or little misfit. However, Type I error rates indicate that the likelihood ratio difference test is not suitable under any of the simulated conditions for evaluating dimensionality in applications of the MRCMLM.

Genetic and Environmental Contributions to Educational Attainment in Australia.

ERIC Educational Resources Information Center

Miller, Paul; Mulvey, Charles; Martin, Nick

2001-01-01

Data from a large sample of Australian twins indicate that 50 to 65 percent of variance in educational attainments can be attributed to genetic endowments. Only about 25 to 40 percent may be due to environmental factors, depending on adjustments for measurement error and assortative mating. (Contains 51 references.) (MLH)

Descriptive Statistical Attributes of Special Education Data Sets

ERIC Educational Resources Information Center

Felder, Valerie

2013-01-01

Micceri (1989) examined the distributional characteristics of 440 large-sample achievement and psychometric measures. All the distributions were found to be nonnormal at alpha = 0.01. Micceri indicated three factors that might contribute to a non-Gaussian error distribution in the population. The first factor is subpopulations within a target…

Mental Representation of Circuit Diagrams: Individual Differences in Procedural Knowledge.

DTIC Science & Technology

1983-12-01

operation. One may know, for example, that a transformer serves to change the voltage of an AC supply, that a particular combination of transitors acts as a...and error measures with respect to overall performance. Even if a large 3-1-- sample could provide statistically significant differences between skill

Modeling Noisy Data with Differential Equations Using Observed and Expected Matrices

ERIC Educational Resources Information Center

Deboeck, Pascal R.; Boker, Steven M.

2010-01-01

Complex intraindividual variability observed in psychology may be well described using differential equations. It is difficult, however, to apply differential equation models in psychological contexts, as time series are frequently short, poorly sampled, and have large proportions of measurement and dynamic error. Furthermore, current methods for…

Factors affecting the sticking of insects on modified aircraft wings

NASA Technical Reports Server (NTRS)

Yi, O.; Chitsaz-Z, M. R.; Eiss, N. S.; Wightman, J. P.

1988-01-01

Previous work showed that the total number of insects sticking to an aluminum surface was reduced by coating the aluminum surface with elastomers. Due to a large number of possible experimental errors, no correlation between the modulus of elasticity, the elastomer, and the total number of insects sticking to a given elastomer was obtained. One of the errors assumed to be introduced during the road test is a variable insect flux so the number of insects striking one surface might be different from that striking another sample. To eliminate this source of error, the road test used to collect insects was simulated in a laboratory by development of an insect impacting technique using a pipe and high pressure compressed air. The insects are accelerated by a compressed air gun to high velocities and are then impacted with a stationary target on which the sample is mounted. The velocity of an object exiting from the pipe was determined and further improvement of the technique was achieved to obtain a uniform air velocity distribution.

Classification based upon gene expression data: bias and precision of error rates.

PubMed

Wood, Ian A; Visscher, Peter M; Mengersen, Kerrie L

2007-06-01

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Lead theft--a study of the "uniqueness" of lead from church roofs.

PubMed

Bond, John W; Hainsworth, Sarah V; Lau, Tien L

2013-07-01

In the United Kingdom, theft of lead is common, particularly from churches and other public buildings with lead roofs. To assess the potential to distinguish lead from different sources, 41 samples of lead from 24 church roofs in Northamptonshire, U.K, have been analyzed for relative abundance of trace elements and isotopes of lead using X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry, respectively. XRF revealed the overall presence of 12 trace elements with the four most abundant, calcium, phosphorus, silicon, and sulfur, showing a large weight percentage standard error of the mean of all samples suggesting variation in the weight percentage of these elements between different church roofs. Multiple samples from the same roofs, but different lead sheets, showed much lower weight percentage standard errors of the mean suggesting similar trace element concentrations. Lead isotope ratios were similar for all samples. Factors likely to affect the occurrence of these trace elements are discussed. © 2013 American Academy of Forensic Sciences.

Quantifying uncertainty in carbon and nutrient pools of coarse woody debris

NASA Astrophysics Data System (ADS)

See, C. R.; Campbell, J. L.; Fraver, S.; Domke, G. M.; Harmon, M. E.; Knoepp, J. D.; Woodall, C. W.

2016-12-01

Woody detritus constitutes a major pool of both carbon and nutrients in forested ecosystems. Estimating coarse wood stocks relies on many assumptions, even when full surveys are conducted. Researchers rarely report error in coarse wood pool estimates, despite the importance to ecosystem budgets and modelling efforts. To date, no study has attempted a comprehensive assessment of error rates and uncertainty inherent in the estimation of this pool. Here, we use Monte Carlo analysis to propagate the error associated with the major sources of uncertainty present in the calculation of coarse wood carbon and nutrient (i.e., N, P, K, Ca, Mg, Na) pools. We also evaluate individual sources of error to identify the importance of each source of uncertainty in our estimates. We quantify sampling error by comparing the three most common field methods used to survey coarse wood (two transect methods and a whole-plot survey). We quantify the measurement error associated with length and diameter measurement, and technician error in species identification and decay class using plots surveyed by multiple technicians. We use previously published values of model error for the four most common methods of volume estimation: Smalian's, conical frustum, conic paraboloid, and average-of-ends. We also use previously published values for error in the collapse ratio (cross-sectional height/width) of decayed logs that serves as a surrogate for the volume remaining. We consider sampling error in chemical concentration and density for all decay classes, using distributions from both published and unpublished studies. Analytical uncertainty is calculated using standard reference plant material from the National Institute of Standards. Our results suggest that technician error in decay classification can have a large effect on uncertainty, since many of the error distributions included in the calculation (e.g. density, chemical concentration, volume-model selection, collapse ratio) are decay-class specific.

Sample allocation balancing overall representativeness and stratum precision.

PubMed

Diaz-Quijano, Fredi Alexander

2018-05-07

In large-scale surveys, it is often necessary to distribute a preset sample size among a number of strata. Researchers must make a decision between prioritizing overall representativeness or precision of stratum estimates. Hence, I evaluated different sample allocation strategies based on stratum size. The strategies evaluated herein included allocation proportional to stratum population; equal sample for all strata; and proportional to the natural logarithm, cubic root, and square root of the stratum population. This study considered the fact that, from a preset sample size, the dispersion index of stratum sampling fractions is correlated with the population estimator error and the dispersion index of stratum-specific sampling errors would measure the inequality in precision distribution. Identification of a balanced and efficient strategy was based on comparing those both dispersion indices. Balance and efficiency of the strategies changed depending on overall sample size. As the sample to be distributed increased, the most efficient allocation strategies were equal sample for each stratum; proportional to the logarithm, to the cubic root, to square root; and that proportional to the stratum population, respectively. Depending on sample size, each of the strategies evaluated could be considered in optimizing the sample to keep both overall representativeness and stratum-specific precision. Copyright © 2018 Elsevier Inc. All rights reserved.

Absolute pitch among students at the Shanghai Conservatory of Music: a large-scale direct-test study.

PubMed

Deutsch, Diana; Li, Xiaonuo; Shen, Jing

2013-11-01

This paper reports a large-scale direct-test study of absolute pitch (AP) in students at the Shanghai Conservatory of Music. Overall note-naming scores were very high, with high scores correlating positively with early onset of musical training. Students who had begun training at age ≤5 yr scored 83% correct not allowing for semitone errors and 90% correct allowing for semitone errors. Performance levels were higher for white key pitches than for black key pitches. This effect was greater for orchestral performers than for pianists, indicating that it cannot be attributed to early training on the piano. Rather, accuracy in identifying notes of different names (C, C#, D, etc.) correlated with their frequency of occurrence in a large sample of music taken from the Western tonal repertoire. There was also an effect of pitch range, so that performance on tones in the two-octave range beginning on Middle C was higher than on tones in the octave below Middle C. In addition, semitone errors tended to be on the sharp side. The evidence also ran counter to the hypothesis, previously advanced by others, that the note A plays a special role in pitch identification judgments.

Recovery of Bennu's orientation for the OSIRIS-REx mission: implications for the spin state accuracy and geolocation errors

NASA Astrophysics Data System (ADS)

Mazarico, Erwan; Rowlands, David D.; Sabaka, Terence J.; Getzandanner, Kenneth M.; Rubincam, David P.; Nicholas, Joseph B.; Moreau, Michael C.

2017-10-01

The goal of the OSIRIS-REx mission is to return a sample of asteroid material from near-Earth asteroid (101955) Bennu. The role of the navigation and flight dynamics team is critical for the spacecraft to execute a precisely planned sampling maneuver over a specifically selected landing site. In particular, the orientation of Bennu needs to be recovered with good accuracy during orbital operations to contribute as small an error as possible to the landing error budget. Although Bennu is well characterized from Earth-based radar observations, its orientation dynamics are not sufficiently known to exclude the presence of a small wobble. To better understand this contingency and evaluate how well the orientation can be recovered in the presence of a large 1° wobble, we conduct a comprehensive simulation with the NASA GSFC GEODYN orbit determination and geodetic parameter estimation software. We describe the dynamic orientation modeling implemented in GEODYN in support of OSIRIS-REx operations and show how both altimetry and imagery data can be used as either undifferenced (landmark, direct altimetry) or differenced (image crossover, altimetry crossover) measurements. We find that these two different types of data contribute differently to the recovery of instrument pointing or planetary orientation. When upweighted, the absolute measurements help reduce the geolocation errors, despite poorer astrometric (inertial) performance. We find that with no wobble present, all the geolocation requirements are met. While the presence of a large wobble is detrimental, the recovery is still reliable thanks to the combined use of altimetry and imagery data.

Enhancing the sensitivity to new physics in the tt¯ invariant mass distribution

NASA Astrophysics Data System (ADS)

Álvarez, Ezequiel

2012-08-01

We propose selection cuts on the LHC tt¯ production sample which should enhance the sensitivity to new physics signals in the study of the tt¯ invariant mass distribution. We show that selecting events in which the tt¯ object has little transverse and large longitudinal momentum enlarges the quark-fusion fraction of the sample and therefore increases its sensitivity to new physics which couples to quarks and not to gluons. We find that systematic error bars play a fundamental role and assume a simple model for them. We check how a non-visible new particle would become visible after the selection cuts enhance its resonance bump. A final realistic analysis should be done by the experimental groups with a correct evaluation of the systematic error bars.

MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data.

PubMed

Fan, Yu; Xi, Liu; Hughes, Daniel S T; Zhang, Jianjun; Zhang, Jianhua; Futreal, P Andrew; Wheeler, David A; Wang, Wenyi

2016-08-24

Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We develop MuSE ( http://bioinformatics.mdanderson.org/main/MuSE ), Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of the tumor and normal tissue at each reference base. MuSE adopts a sample-specific error model that reflects the underlying tumor heterogeneity to greatly improve the overall accuracy. We demonstrate the accuracy of MuSE in calling subclonal mutations in the context of large-scale tumor sequencing projects using whole exome and whole genome sequencing.

Quantifying Uncertainty in Instantaneous Orbital Data Products of TRMM over Indian Subcontinent

NASA Astrophysics Data System (ADS)

Jayaluxmi, I.; Nagesh, D.

2013-12-01

In the last 20 years, microwave radiometers have taken satellite images of earth's weather proving to be a valuable tool for quantitative estimation of precipitation from space. However, along with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. While most of the uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year), evaluation of instantaneous rainfall intensities from satellite orbital data products are relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. Especially over land regions, where the highly varying land surface emissivity offer a myriad of complications hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present study fosters the development of uncertainty analysis using instantaneous satellite orbital data products (version 7 of 1B11, 2A25, 2A23) derived from the passive and active sensors onboard Tropical Rainfall Measuring Mission (TRMM) satellite, namely TRMM microwave imager (TMI) and Precipitation Radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Analysis conducted over the land regions of India investigates three sources of uncertainty in detail. These include 1) Errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) Uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) Sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. Case study results obtained during the Indian summer monsoon months of June-September are presented using contingency table statistics, performance diagram, scatter plots and probability density functions. Our study demonstrates that theory of copula can be efficiently used to represent the highly non linear dependency structure of rainfall with respect to TMI low frequency channels of 19, 21, 37 GHz. This questions the exclusive usage of high frequency 85 GHz channel for TMI overland rainfall retrieval algorithms. Further, the PR sampling errors revealed using a statistical bootstrap technique was found to incur relative sampling errors <30% (for 2 degree grids) over India whose magnitudes were biased towards stratiform rainfall type and sampling technique employed. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications for decision making prior to incorporating the resulting orbital products for basin scale hydrologic modeling.

High variability in strain estimation errors when using a commercial ultrasound speckle tracking algorithm on tendon tissue.

PubMed

Fröberg, Åsa; Mårtensson, Mattias; Larsson, Matilda; Janerot-Sjöberg, Birgitta; D'Hooge, Jan; Arndt, Anton

2016-10-01

Ultrasound speckle tracking offers a non-invasive way of studying strain in the free Achilles tendon where no anatomical landmarks are available for tracking. This provides new possibilities for studying injury mechanisms during sport activity and the effects of shoes, orthotic devices, and rehabilitation protocols on tendon biomechanics. To investigate the feasibility of using a commercial ultrasound speckle tracking algorithm for assessing strain in tendon tissue. A polyvinyl alcohol (PVA) phantom, three porcine tendons, and a human Achilles tendon were mounted in a materials testing machine and loaded to 4% peak strain. Ultrasound long-axis cine-loops of the samples were recorded. Speckle tracking analysis of axial strain was performed using a commercial speckle tracking software. Estimated strain was then compared to reference strain known from the materials testing machine. Two frame rates and two region of interest (ROI) sizes were evaluated. Best agreement between estimated strain and reference strain was found in the PVA phantom (absolute error in peak strain: 0.21 ± 0.08%). The absolute error in peak strain varied between 0.72 ± 0.65% and 10.64 ± 3.40% in the different tendon samples. Strain determined with a frame rate of 39.4 Hz had lower errors than 78.6 Hz as was the case with a 22 mm compared to an 11 mm ROI. Errors in peak strain estimation showed high variability between tendon samples and were large in relation to strain levels previously described in the Achilles tendon. © The Foundation Acta Radiologica 2016.

Assessment of Systematic Chromatic Errors that Impact Sub-1% Photometric Precision in Large-Area Sky Surveys

DOE PAGES

Li, T. S.; DePoy, D. L.; Marshall, J. L.; ...

2016-06-01

Here, we report that meeting the science goals for many current and future ground-based optical large-area sky surveys requires that the calibrated broadband photometry is both stable in time and uniform over the sky to 1% precision or better. Past and current surveys have achieved photometric precision of 1%–2% by calibrating the survey's stellar photometry with repeated measurements of a large number of stars observed in multiple epochs. The calibration techniques employed by these surveys only consider the relative frame-by-frame photometric zeropoint offset and the focal plane position-dependent illumination corrections, which are independent of the source color. However, variations inmore » the wavelength dependence of the atmospheric transmission and the instrumental throughput induce source color-dependent systematic errors. These systematic errors must also be considered to achieve the most precise photometric measurements. In this paper, we examine such systematic chromatic errors (SCEs) using photometry from the Dark Energy Survey (DES) as an example. We first define a natural magnitude system for DES and calculate the systematic errors on stellar magnitudes when the atmospheric transmission and instrumental throughput deviate from the natural system. We conclude that the SCEs caused by the change of airmass in each exposure, the change of the precipitable water vapor and aerosol in the atmosphere over time, and the non-uniformity of instrumental throughput over the focal plane can be up to 2% in some bandpasses. We then compare the calculated SCEs with the observed DES data. For the test sample data, we correct these errors using measurements of the atmospheric transmission and instrumental throughput from auxiliary calibration systems. In conclusion, the residual after correction is less than 0.3%. Moreover, we calculate such SCEs for Type Ia supernovae and elliptical galaxies and find that the chromatic errors for non-stellar objects are redshift-dependent and can be larger than those for stars at certain redshifts.« less

Research on the magnetorheological finishing (MRF) technology with dual polishing heads

NASA Astrophysics Data System (ADS)

Huang, Wen; Zhang, Yunfei; He, Jianguo; Zheng, Yongcheng; Luo, Qing; Hou, Jing; Yuan, Zhigang

2014-08-01

Magnetorheological finishing (MRF) is a key polishing technique capable of rapidly converging to the required surface figure. Due to the deficiency of general one-polishing-head MRF technology, a dual polishing heads MRF technology was studied and a dual polishing heads MRF machine with 8 axes was developed. The machine has the ability to manufacture large aperture optics with high figure accuracy. The large polishing head is suitable for polishing large aperture optics, controlling large spatial length's wave structures, correcting low-medium frequency errors with high removal rates. While the small polishing head has more advantages in manufacturing small aperture optics, controlling small spatial wavelength's wave structures, correcting mid-high frequency and removing nanoscale materials. Material removal characteristic and figure correction ability for each of large and small polishing head was studied. Each of two polishing heads respectively acquired stable and valid polishing removal function and ultra-precision flat sample. After a single polishing iteration using small polishing head, the figure error in 45mm diameter of a 50 mm diameter plano optics was significantly improved from 0.21λ to 0.08λ by PV (RMS 0.053λ to 0.015λ). After three polishing iterations using large polishing head , the figure error in 410mm×410mm of a 430mm×430mm large plano optics was significantly improved from 0.40λ to 0.10λ by PV (RMS 0.068λ to 0.013λ) .This results show that the dual polishing heads MRF machine not only have good material removal stability, but also excellent figure correction capability.

Ray tracing method for the evaluation of grazing incidence x-ray telescopes described by spatially sampled surfaces.

PubMed

Yu, Jun; Shen, Zhengxiang; Sheng, Pengfeng; Wang, Xiaoqiang; Hailey, Charles J; Wang, Zhanshan

2018-03-01

The nested grazing incidence telescope can achieve a large collecting area in x-ray astronomy, with a large number of closely packed, thin conical mirrors. Exploiting the surface metrological data, the ray tracing method used to reconstruct the shell surface topography and evaluate the imaging performance is a powerful tool to assist iterative improvement in the fabrication process. However, current two-dimensional (2D) ray tracing codes, especially when utilized with densely sampled surface shape data, may not provide sufficient accuracy of reconstruction and are computationally cumbersome. In particular, 2D ray tracing currently employed considers coplanar rays and thus simulates only these rays along the meridional plane. This captures axial figure errors but leaves other important errors, such as roundness errors, unaccounted for. We introduce a semianalytic, three-dimensional (3D) ray tracing approach for x-ray optics that overcomes these shortcomings. And the present method is both computationally fast and accurate. We first introduce the principles and the computational details of this 3D ray tracing method. Then the computer simulations of this approach compared to 2D ray tracing are demonstrated, using an ideal conic Wolter-I telescope for benchmarking. Finally, the present 3D ray tracing is used to evaluate the performance of a prototype x-ray telescope fabricated for the enhanced x-ray timing and polarization mission.

Large Sample Confidence Intervals for Item Response Theory Reliability Coefficients

ERIC Educational Resources Information Center

Andersson, Björn; Xin, Tao

2018-01-01

In applications of item response theory (IRT), an estimate of the reliability of the ability estimates or sum scores is often reported. However, analytical expressions for the standard errors of the estimators of the reliability coefficients are not available in the literature and therefore the variability associated with the estimated reliability…

Can standard cosmological models explain the observed Abell cluster bulk flow?

NASA Technical Reports Server (NTRS)

Strauss, Michael A.; Cen, Renyue; Ostriker, Jeremiah P.; Laure, Tod R.; Postman, Marc

1995-01-01

Lauer and Postman (LP) observed that all Abell clusters with redshifts less than 15,000 km/s appear to be participating in a bulk flow of 689 km/s with respect to the cosmic microwave background. We find this result difficult to reconcile with all popular models for large-scale structure formation that assume Gaussian initial conditions. This conclusion is based on Monte Carlo realizations of the LP data, drawn from large particle-mesh N-body simulations for six different models of the initial power spectrum (standard, tilted, and Omega(sub 0) = 0.3 cold dark matter, and two variants of the primordial baryon isocurvature model). We have taken special care to treat properly the longest-wavelength components of the power spectra. The simulations are sampled, 'observed,' and analyzed as identically as possible to the LP cluster sample. Large-scale bulk flows as measured from clusters in the simulations are in excellent agreement with those measured from the grid: the clusters do not exhibit any strong velocity bias on large scales. Bulk flows with amplitude as large as that reported by LP are not uncommon in the Monte Carlo data stes; the distribution of measured bulk flows before error bias subtraction is rougly Maxwellian, with a peak around 400 km/s. However the chi squared of the observed bulk flow, taking into account the anisotropy of the error ellipsoid, is much more difficult to match in the simulations. The models examined are ruled out at confidence levels between 94% and 98%.

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

PubMed

Sankari, E Siva; Manimegalai, D

2017-12-21

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

Location tests for biomarker studies: a comparison using simulations for the two-sample case.

PubMed

Scheinhardt, M O; Ziegler, A

2013-01-01

Gene, protein, or metabolite expression levels are often non-normally distributed, heavy tailed and contain outliers. Standard statistical approaches may fail as location tests in this situation. In three Monte-Carlo simulation studies, we aimed at comparing the type I error levels and empirical power of standard location tests and three adaptive tests [O'Gorman, Can J Stat 1997; 25: 269 -279; Keselman et al., Brit J Math Stat Psychol 2007; 60: 267- 293; Szymczak et al., Stat Med 2013; 32: 524 - 537] for a wide range of distributions. We simulated two-sample scenarios using the g-and-k-distribution family to systematically vary tail length and skewness with identical and varying variability between groups. All tests kept the type I error level when groups did not vary in their variability. The standard non-parametric U-test performed well in all simulated scenarios. It was outperformed by the two non-parametric adaptive methods in case of heavy tails or large skewness. Most tests did not keep the type I error level for skewed data in the case of heterogeneous variances. The standard U-test was a powerful and robust location test for most of the simulated scenarios except for very heavy tailed or heavy skewed data, and it is thus to be recommended except for these cases. The non-parametric adaptive tests were powerful for both normal and non-normal distributions under sample variance homogeneity. But when sample variances differed, they did not keep the type I error level. The parametric adaptive test lacks power for skewed and heavy tailed distributions.

Low energy atmospheric muon neutrinos in MACRO

NASA Astrophysics Data System (ADS)

Ambrosio, M.; Antolini, R.; Auriemma, G.; Bakari, D.; Baldini, A.; Barbarino, G. C.; Barish, B. C.; Battistoni, G.; Bellotti, R.; Bemporad, C.; Bernardini, P.; Bilokon, H.; Bisi, V.; Bloise, C.; Bower, C.; Brigida, M.; Bussino, S.; Cafagna, F.; Calicchio, M.; Campana, D.; Carboni, M.; Cecchini, S.; Cei, F.; Chiarella, V.; Choudhary, B. C.; Coutu, S.; De Cataldo, G.; Dekhissi, H.; De Marzo, C.; De Mitri, I.; Derkaoui, J.; De Vincenzi, M.; Di Credico, A.; Erriquez, O.; Favuzzi, C.; Forti, C.; Fusco, P.; Giacomelli, G.; Giannini, G.; Giglietto, N.; Giorgini, M.; Grassi, M.; Gray, L.; Grillo, A.; Guarino, F.; Gustavino, C.; Habig, A.; Hanson, K.; Heinz, R.; Iarocci, E.; Katsavounidis, E.; Katsavounidis, I.; Kearns, E.; Kim, H.; Kyriazopoulou, S.; Lamanna, E.; Lane, C.; Levin, D. S.; Lipari, P.; Longley, N. P.; Longo, M. J.; Loparco, F.; Maaroufi, F.; Mancarella, G.; Mandrioli, G.; Margiotta, A.; Marini, A.; Martello, D.; Marzari-Chiesa, A.; Mazziotta, M. N.; Michael, D. G.; Mikheyev, S.; Miller, L.; Monacelli, P.; Montaruli, T.; Monteno, M.; Mufson, S.; Musser, J.; Nicolò, D.; Nolty, R.; Orth, C.; Osteria, G.; Ouchrif, M.; Palamara, O.; Patera, V.; Patrizii, L.; Pazzi, R.; Peck, C. W.; Perrone, L.; Petrera, S.; Pistilli, P.; Popa, V.; Rainò, A.; Reynoldson, J.; Ronga, F.; Satriano, C.; Satta, L.; Scapparone, E.; Scholberg, K.; Sciubba, A.; Serra, P.; Sioli, M.; Sirri, G.; Sitta, M.; Spinelli, P.; Spinetti, M.; Spurio, M.; Steinberg, R.; Stone, J. L.; Sulak, L. R.; Surdo, A.; Tarlè, G.; Togo, V.; Vakili, M.; Vilela, E.; Walter, C. W.; Webb, R.

2000-04-01

We present the measurement of two event samples induced by atmospheric νμ of average energy Eoverlineν~4 GeV. In the first sample, a neutrino interacts inside the MACRO detector producing an upward-going muon leaving the apparatus. The ratio of the number of observed to expected events is 0.57+/-0.05stat+/-0.06syst+/-0.14theor with an angular distribution similar to that expected from the Bartol atmospheric neutrino flux. The second is a mixed sample of internally produced downward-going muons and externally produced upward-going muons stopping inside the detector. These two subsamples are selected by topological criteria; the lack of timing information makes it impossible to distinguish stopping from downgoing muons. The ratio of the number of observed to expected events is 0.71+/-0.05stat+/-0.07syst+/-0.18theor. The observed deficits in each subsample is in agreement with neutrino oscillations, although the significance is reduced by the large theoretical errors. However, the ratio of the two samples causes a large cancellation of theoretical and of some systematic errors. With the ratio, we rule out the no-oscillation hypothesis at 95% c.l. Furthermore, the ratio tests the pathlength dependence of possible oscillations. The data of both samples and their ratio favor maximal mixing and Δm2~10-3-10-2 eV2. These parameters are in agreement with our results from upward throughgoing muons, induced by νμ of much higher energies.

Potential of near-infrared hyperspectral reflectance imaging for screening of farm feed contamination

NASA Astrophysics Data System (ADS)

Wang, Wenbo; Paliwal, Jitendra

2005-09-01

With the outbreak of Bovine Spongiform Encephalopathy (BSE) (commonly known as mad cow disease) in 1987 in the United Kingdom and a recent case discovered in Alberta, more and more emphasis is placed on food and farm feed quality and safety issues internationally. The disease is believed to be spread through farm feed contamination by animal byproducts in the form of meat-and-bone-meal (MBM). The paper reviewed the available techniques necessary to the enforcement of legislation concerning the feed safety issues. The standard microscopy method, although highly sensitive, is laborious and costly. A method to routinely screen farm feed contamination certainly helps to reduce the complexity of safety inspection. A hyperspectral imaging system working in the near-infrared wavelength region of 1100-1600 nm was used to study the possibility of detection of ground broiler feed contamination by ground pork. Hyperspectral images of raw broiler feed, ground broiler feed, ground pork, and contaminated feed samples were acquired. Raw broiler feed samples were found to possess comparatively large spectral variations due to light scattering effect. Ground feed adulterated with 1%, 3%, 5%, and 10% of ground pork was tested to identify feed contamination. Discriminant analysis using Mahalanobis distance showed that the model trained using pure ground feed samples and pure ground pork samples resulted in 100% false negative errors for all test replicates of contaminated samples. A discriminant model trained with pure ground feed samples and 10% contamination level samples resulted in 12.5% false positive error and 0% false negative error.

Diagnosing and Correcting Mass Accuracy and Signal Intensity Error Due to Initial Ion Position Variations in a MALDI TOFMS

NASA Astrophysics Data System (ADS)

Malys, Brian J.; Piotrowski, Michelle L.; Owens, Kevin G.

2018-02-01

Frustrated by worse than expected error for both peak area and time-of-flight (TOF) in matrix assisted laser desorption ionization (MALDI) experiments using samples prepared by electrospray deposition, it was finally determined that there was a correlation between sample location on the target plate and the measured TOF/peak area. Variations in both TOF and peak area were found to be due to small differences in the initial position of ions formed in the source region of the TOF mass spectrometer. These differences arise largely from misalignment of the instrument sample stage, with a smaller contribution arising from the non-ideal shape of the target plates used. By physically measuring the target plates used and comparing TOF data collected from three different instruments, an estimate of the magnitude and direction of the sample stage misalignment was determined for each of the instruments. A correction method was developed to correct the TOFs and peak areas obtained for a given combination of target plate and instrument. Two correction factors are determined, one by initially collecting spectra from each sample position used and another by using spectra from a single position for each set of samples on a target plate. For TOF and mass values, use of the correction factor reduced the error by a factor of 4, with the relative standard deviation (RSD) of the corrected masses being reduced to 12-24 ppm. For the peak areas, the RSD was reduced from 28% to 16% for samples deposited twice onto two target plates over two days.

Diagnosing and Correcting Mass Accuracy and Signal Intensity Error Due to Initial Ion Position Variations in a MALDI TOFMS

NASA Astrophysics Data System (ADS)

Malys, Brian J.; Piotrowski, Michelle L.; Owens, Kevin G.

2017-12-01

Frustrated by worse than expected error for both peak area and time-of-flight (TOF) in matrix assisted laser desorption ionization (MALDI) experiments using samples prepared by electrospray deposition, it was finally determined that there was a correlation between sample location on the target plate and the measured TOF/peak area. Variations in both TOF and peak area were found to be due to small differences in the initial position of ions formed in the source region of the TOF mass spectrometer. These differences arise largely from misalignment of the instrument sample stage, with a smaller contribution arising from the non-ideal shape of the target plates used. By physically measuring the target plates used and comparing TOF data collected from three different instruments, an estimate of the magnitude and direction of the sample stage misalignment was determined for each of the instruments. A correction method was developed to correct the TOFs and peak areas obtained for a given combination of target plate and instrument. Two correction factors are determined, one by initially collecting spectra from each sample position used and another by using spectra from a single position for each set of samples on a target plate. For TOF and mass values, use of the correction factor reduced the error by a factor of 4, with the relative standard deviation (RSD) of the corrected masses being reduced to 12-24 ppm. For the peak areas, the RSD was reduced from 28% to 16% for samples deposited twice onto two target plates over two days. [Figure not available: see fulltext.

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data

USGS Publications Warehouse

Junttila, Virpi; Kauranne, Tuomo; Finley, Andrew O.; Bradford, John B.

2015-01-01

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.

Accuracy and precision of Legionella isolation by US laboratories in the ELITE program pilot study.

PubMed

Lucas, Claressa E; Taylor, Thomas H; Fields, Barry S

2011-10-01

A pilot study for the Environmental Legionella Isolation Techniques Evaluation (ELITE) Program, a proficiency testing scheme for US laboratories that culture Legionella from environmental samples, was conducted September 1, 2008 through March 31, 2009. Participants (n=20) processed panels consisting of six sample types: pure and mixed positive, pure and mixed negative, pure and mixed variable. The majority (93%) of all samples (n=286) were correctly characterized, with 88.5% of samples positive for Legionella and 100% of negative samples identified correctly. Variable samples were incorrectly identified as negative in 36.9% of reports. For all samples reported positive (n=128), participants underestimated the cfu/ml by a mean of 1.25 logs with standard deviation of 0.78 logs, standard error of 0.07 logs, and a range of 3.57 logs compared to the CDC re-test value. Centering results around the interlaboratory mean yielded a standard deviation of 0.65 logs, standard error of 0.06 logs, and a range of 3.22 logs. Sampling protocol, treatment regimen, culture procedure, and laboratory experience did not significantly affect the accuracy or precision of reported concentrations. Qualitative and quantitative results from the ELITE pilot study were similar to reports from a corresponding proficiency testing scheme available in the European Union, indicating these results are probably valid for most environmental laboratories worldwide. The large enumeration error observed suggests that the need for remediation of a water system should not be determined solely by the concentration of Legionella observed in a sample since that value is likely to underestimate the true level of contamination. Published by Elsevier Ltd.

Abundance Ratios in a Large Sample of Emps with VLT+UVES

NASA Astrophysics Data System (ADS)

Hill, Vanessa; Cayrel, Roger; Spite, Monique; Bonifacio, Piercarlo; Eric, Depagne; Patrick, François; Timothy, Beers C.; Johannes, Andersen; Beatriz, Barbuy; Birgitta, Nordström

Constraints on Early Galactic Enrichement from a large sample of Extremely Metal Poor Stars I will present the overall results from an large effort conducted at ESO-VLT+UVES to measure abundances in a sample of extremely metal-poor stars (EMPS) from high-resolution and high signal to noise spectra. More than 70 EMPS with [Fe/H]<-2.7 were observed equally distributed between turnoff and giants stars and very precise abundance ratios could be derived thanks to the high quality of the data. Among the results those of specific interest are lithium measurements in unevolved EMPS the much debated abundance of oxygen in the early galaxy (we present [OI] line measurements down to [O/Fe]=-3.5) and the trends of alpha elements iron group elements and Zinc. The scatter around these trends will also be discussed taking advantage of the small observationnal error-bars of this dataset. The implications on the early Galactic enrichement will be rewiewed while more specific topics covered by this large effort (and large team) will be adressed in devoted posters.

Relationship auditing of the FMA ontology

PubMed Central

Gu, Huanying (Helen); Wei, Duo; Mejino, Jose L.V.; Elhanan, Gai

2010-01-01

The Foundational Model of Anatomy (FMA) ontology is a domain reference ontology based on a disciplined modeling approach. Due to its large size, semantic complexity and manual data entry process, errors and inconsistencies are unavoidable and might remain within the FMA structure without detection. In this paper, we present computable methods to highlight candidate concepts for various relationship assignment errors. The process starts with locating structures formed by transitive structural relationships (part_of, tributary_of, branch_of) and examine their assignments in the context of the IS-A hierarchy. The algorithms were designed to detect five major categories of possible incorrect relationship assignments: circular, mutually exclusive, redundant, inconsistent, and missed entries. A domain expert reviewed samples of these presumptive errors to confirm the findings. Seven thousand and fifty-two presumptive errors were detected, the largest proportion related to part_of relationship assignments. The results highlight the fact that errors are unavoidable in complex ontologies and that well designed algorithms can help domain experts to focus on concepts with high likelihood of errors and maximize their effort to ensure consistency and reliability. In the future similar methods might be integrated with data entry processes to offer real-time error detection. PMID:19475727

Predicting ambient aerosol thermal-optical reflectance (TOR) measurements from infrared spectra: organic carbon

NASA Astrophysics Data System (ADS)

Dillner, A. M.; Takahama, S.

2015-03-01

Organic carbon (OC) can constitute 50% or more of the mass of atmospheric particulate matter. Typically, organic carbon is measured from a quartz fiber filter that has been exposed to a volume of ambient air and analyzed using thermal methods such as thermal-optical reflectance (TOR). Here, methods are presented that show the feasibility of using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE or Teflon) filters to accurately predict TOR OC. This work marks an initial step in proposing a method that can reduce the operating costs of large air quality monitoring networks with an inexpensive, non-destructive analysis technique using routinely collected PTFE filter samples which, in addition to OC concentrations, can concurrently provide information regarding the composition of organic aerosol. This feasibility study suggests that the minimum detection limit and errors (or uncertainty) of FT-IR predictions are on par with TOR OC such that evaluation of long-term trends and epidemiological studies would not be significantly impacted. To develop and test the method, FT-IR absorbance spectra are obtained from 794 samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011. Partial least-squares regression is used to calibrate sample FT-IR absorbance spectra to TOR OC. The FTIR spectra are divided into calibration and test sets by sampling site and date. The calibration produces precise and accurate TOR OC predictions of the test set samples by FT-IR as indicated by high coefficient of variation (R2; 0.96), low bias (0.02 μg m-3, the nominal IMPROVE sample volume is 32.8 m3), low error (0.08 μg m-3) and low normalized error (11%). These performance metrics can be achieved with various degrees of spectral pretreatment (e.g., including or excluding substrate contributions to the absorbances) and are comparable in precision to collocated TOR measurements. FT-IR spectra are also divided into calibration and test sets by OC mass and by OM / OC ratio, which reflects the organic composition of the particulate matter and is obtained from organic functional group composition; these divisions also leads to precise and accurate OC predictions. Low OC concentrations have higher bias and normalized error due to TOR analytical errors and artifact-correction errors, not due to the range of OC mass of the samples in the calibration set. However, samples with low OC mass can be used to predict samples with high OC mass, indicating that the calibration is linear. Using samples in the calibration set that have different OM / OC or ammonium / OC distributions than the test set leads to only a modest increase in bias and normalized error in the predicted samples. We conclude that FT-IR analysis with partial least-squares regression is a robust method for accurately predicting TOR OC in IMPROVE network samples - providing complementary information to the organic functional group composition and organic aerosol mass estimated previously from the same set of sample spectra (Ruthenburg et al., 2014).

A simple differential steady-state method to measure the thermal conductivity of solid bulk materials with high accuracy.

PubMed

Kraemer, D; Chen, G

2014-02-01

Accurate measurements of thermal conductivity are of great importance for materials research and development. Steady-state methods determine thermal conductivity directly from the proportionality between heat flow and an applied temperature difference (Fourier Law). Although theoretically simple, in practice, achieving high accuracies with steady-state methods is challenging and requires rather complex experimental setups due to temperature sensor uncertainties and parasitic heat loss. We developed a simple differential steady-state method in which the sample is mounted between an electric heater and a temperature-controlled heat sink. Our method calibrates for parasitic heat losses from the electric heater during the measurement by maintaining a constant heater temperature close to the environmental temperature while varying the heat sink temperature. This enables a large signal-to-noise ratio which permits accurate measurements of samples with small thermal conductance values without an additional heater calibration measurement or sophisticated heater guards to eliminate parasitic heater losses. Additionally, the differential nature of the method largely eliminates the uncertainties of the temperature sensors, permitting measurements with small temperature differences, which is advantageous for samples with high thermal conductance values and/or with strongly temperature-dependent thermal conductivities. In order to accelerate measurements of more than one sample, the proposed method allows for measuring several samples consecutively at each temperature measurement point without adding significant error. We demonstrate the method by performing thermal conductivity measurements on commercial bulk thermoelectric Bi2Te3 samples in the temperature range of 30-150 °C with an error below 3%.

Inferring Population Size History from Large Samples of Genome-Wide Molecular Data - An Approximate Bayesian Computation Approach

PubMed Central

Boitard, Simon; Rodríguez, Willy; Jay, Flora; Mona, Stefano; Austerlitz, Frédéric

2016-01-01

Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey), PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles. PMID:26943927

How well do we know the infaunal biomass of the continental shelf?

NASA Astrophysics Data System (ADS)

Powell, Eric N.; Mann, Roger

2016-03-01

Benthic infauna comprise a wide range of taxa of varying abundances and sizes, but large infaunal taxa are infrequently recorded in community surveys of the shelf benthos. These larger, but numerically rare, species may contribute disproportionately to biomass, however. We examine the degree to which standard benthic sampling gear and survey design provide an adequate estimate of the biomass of large infauna using the Atlantic surfclam, Spisula solidissima, on the continental shelf off the northeastern coast of the United States as a test organism. We develop a numerical model that simulates standard survey designs, gear types, and sampling densities to evaluate the effectiveness of vertically-dropped sampling gear (e.g., boxcores, grabs) for estimating density of large species. Simulations of randomly distributed clams at a density of 0.5-1 m-2 within an 0.25-km2 domain show that lower sampling densities (1-5 samples per sampling event) resulted in highly inaccurate estimates of clam density with the presence of clams detected in less than 25% of the sampling events. In all cases in which patchiness was present in the simulated clam population, surveys were prone to very large errors (survey availability events) unless a dense (e.g., 100-sample) sampling protocol was imposed. Thus, commercial quantities of surfclams could easily go completely undetected by any standard benthic community survey protocol using vertically-dropped gear. Without recourse to modern high-volume sampling gear capable of sampling many meters at a swath, such as hydraulic dredges, biomass of the continental shelf will be grievously underestimated if large infauna are present even at moderate densities.

Defining And Characterizing Sample Representativeness For DWPF Melter Feed Samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shine, E. P.; Poirier, M. R.

2013-10-29

Representative sampling is important throughout the Defense Waste Processing Facility (DWPF) process, and the demonstrated success of the DWPF process to achieve glass product quality over the past two decades is a direct result of the quality of information obtained from the process. The objective of this report was to present sampling methods that the Savannah River Site (SRS) used to qualify waste being dispositioned at the DWPF. The goal was to emphasize the methodology, not a list of outcomes from those studies. This methodology includes proven methods for taking representative samples, the use of controlled analytical methods, and datamore » interpretation and reporting that considers the uncertainty of all error sources. Numerous sampling studies were conducted during the development of the DWPF process and still continue to be performed in order to evaluate options for process improvement. Study designs were based on use of statistical tools applicable to the determination of uncertainties associated with the data needs. Successful designs are apt to be repeated, so this report chose only to include prototypic case studies that typify the characteristics of frequently used designs. Case studies have been presented for studying in-tank homogeneity, evaluating the suitability of sampler systems, determining factors that affect mixing and sampling, comparing the final waste glass product chemical composition and durability to that of the glass pour stream sample and other samples from process vessels, and assessing the uniformity of the chemical composition in the waste glass product. Many of these studies efficiently addressed more than one of these areas of concern associated with demonstrating sample representativeness and provide examples of statistical tools in use for DWPF. The time when many of these designs were implemented was in an age when the sampling ideas of Pierre Gy were not as widespread as they are today. Nonetheless, the engineers and statisticians used carefully thought out designs that systematically and economically provided plans for data collection from the DWPF process. Key shared features of the sampling designs used at DWPF and the Gy sampling methodology were the specification of a standard for sample representativeness, an investigation that produced data from the process to study the sampling function, and a decision framework used to assess whether the specification was met based on the data. Without going into detail with regard to the seven errors identified by Pierre Gy, as excellent summaries are readily available such as Pitard [1989] and Smith [2001], SRS engineers understood, for example, that samplers can be biased (Gy's extraction error), and developed plans to mitigate those biases. Experiments that compared installed samplers with more representative samples obtained directly from the tank may not have resulted in systematically partitioning sampling errors into the now well-known error categories of Gy, but did provide overall information on the suitability of sampling systems. Most of the designs in this report are related to the DWPF vessels, not the large SRS Tank Farm tanks. Samples from the DWPF Slurry Mix Evaporator (SME), which contains the feed to the DWPF melter, are characterized using standardized analytical methods with known uncertainty. The analytical error is combined with the established error from sampling and processing in DWPF to determine the melter feed composition. This composition is used with the known uncertainty of the models in the Product Composition Control System (PCCS) to ensure that the wasteform that is produced is comfortably within the acceptable processing and product performance region. Having the advantage of many years of processing that meets the waste glass product acceptance criteria, the DWPF process has provided a considerable amount of data about itself in addition to the data from many special studies. Demonstrating representative sampling directly from the large Tank Farm tanks is a difficult, if not unsolvable enterprise due to limited accessibility. However, the consistency and the adequacy of sampling and mixing at SRS could at least be studied under the controlled process conditions based on samples discussed by Ray and others [2012a] in Waste Form Qualification Report (WQR) Volume 2 and the transfers from Tanks 40H and 51H to the Sludge Receipt and Adjustment Tank (SRAT) within DWPF. It is important to realize that the need for sample representativeness becomes more stringent as the material gets closer to the melter, and the tanks within DWPF have been studied extensively to meet those needs.« less

Center-to-Limb Variation of Deprojection Errors in SDO/HMI Vector Magnetograms

NASA Astrophysics Data System (ADS)

Falconer, David; Moore, Ronald; Barghouty, Nasser; Tiwari, Sanjiv K.; Khazanov, Igor

2015-04-01

For use in investigating the magnetic causes of coronal heating in active regions and for use in forecasting an active region’s productivity of major CME/flare eruptions, we have evaluated various sunspot-active-region magnetic measures (e.g., total magnetic flux, free-magnetic-energy proxies, magnetic twist measures) from HMI Active Region Patches (HARPs) after the HARP has been deprojected to disk center. From a few tens of thousand HARP vector magnetograms (of a few hundred sunspot active regions) that have been deprojected to disk center, we have determined that the errors in the whole-HARP magnetic measures from deprojection are negligibly small for HARPS deprojected from distances out to 45 heliocentric degrees. For some purposes the errors from deprojection are tolerable out to 60 degrees. We obtained this result by the following process. For each whole-HARP magnetic measure: 1) for each HARP disk passage, normalize the measured values by the measured value for that HARP at central meridian; 2) then for each 0.05 Rs annulus, average the values from all the HARPs in the annulus. This results in an average normalized value as a function of radius for each measure. Assuming no deprojection errors and that, among a large set of HARPs, the measure is as likely to decrease as to increase with HARP distance from disk center, the average of each annulus is expected to be unity, and, for a statistically large sample, the amount of deviation of the average from unity estimates the error from deprojection effects. The deprojection errors arise from 1) errors in the transverse field being deprojected into the vertical field for HARPs observed at large distances from disk center, 2) increasingly larger foreshortening at larger distances from disk center, and 3) possible errors in transverse-field-direction ambiguity resolution.From the compiled set of measured vales of whole-HARP magnetic nonpotentiality parameters measured from deprojected HARPs, we have examined the relation between each nonpotentiality parameter and the speed of CMEs from the measured active regions. For several different nonpotentiality parameters we find there is an upper limit to the CME speed, the limit increasing as the value of the parameter increases.

Optimal estimation of large structure model errors. [in Space Shuttle controller design

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1979-01-01

In-flight estimation of large structure model errors is usually required as a means of detecting inevitable deficiencies in large structure controller/estimator models. The present paper deals with a least-squares formulation which seeks to minimize a quadratic functional of the model errors. The properties of these error estimates are analyzed. It is shown that an arbitrary model error can be decomposed as the sum of two components that are orthogonal in a suitably defined function space. Relations between true and estimated errors are defined. The estimates are found to be approximations that retain many of the significant dynamics of the true model errors. Current efforts are directed toward application of the analytical results to a reference large structure model.

Value Focused Thinking Approach Using Multivariate Validation for Junior Enlisted Performance Reporting in the United States Air Force

DTIC Science & Technology

2014-03-22

consideration for enlisted airmen, has largely become a non factor due to over-inflated scores, with other factors such as specialty knowledge test scores, time...appraisal. Secondly, an Artificial Neural Network (ANN) classifier will be applied to the large sample data to confirm that the values solicited to...jobs, employees make themselves vulnerable to the organization when they expend effort. If extra effort is expended to reduce errors or defects, or

Drifter observations of submesoscale flow kinematics in the coastal ocean

NASA Astrophysics Data System (ADS)

Ohlmann, J. C.; Molemaker, M. J.; Baschek, B.; Holt, B.; Marmorino, G.; Smith, G.

2017-01-01

Fronts and eddies identified with aerial guidance are seeded with drifters to quantify submesoscale flow kinematics. The Lagrangian observations show mean divergence and vorticity values that can exceed 5 times the Coriolis frequency. Values are the largest observed in the field to date and represent an extreme departure from geostrophic dynamics. The study also quantifies errors and biases associated with Lagrangian observations of the underlying velocity strain tensor. The greatest error results from undersampling, even with a large number of drifters. A significant bias comes from inhomogeneous sampling of convergent regions that accumulate drifters within a few hours of deployment. The study demonstrates a Lagrangian sampling paradigm for targeted submesoscale structures over a broad range of scales and presents flow kinematic values associated with vertical velocities O(10) m h-1 that can have profound implications on ocean biogeochemistry.

Steady-state low thermal resistance characterization apparatus: The bulk thermal tester

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burg, Brian R.; Kolly, Manuel; Blasakis, Nicolas

The reliability of microelectronic devices is largely dependent on electronic packaging, which includes heat removal. The appropriate packaging design therefore necessitates precise knowledge of the relevant material properties, including thermal resistance and thermal conductivity. Thin materials and high conductivity layers make their thermal characterization challenging. A steady state measurement technique is presented and evaluated with the purpose to characterize samples with a thermal resistance below 100 mm{sup 2} K/W. It is based on the heat flow meter bar approach made up by two copper blocks and relies exclusively on temperature measurements from thermocouples. The importance of thermocouple calibration is emphasizedmore » in order to obtain accurate temperature readings. An in depth error analysis, based on Gaussian error propagation, is carried out. An error sensitivity analysis highlights the importance of the precise knowledge of the thermal interface materials required for the measurements. Reference measurements on Mo samples reveal a measurement uncertainty in the range of 5% and most accurate measurements are obtained at high heat fluxes. Measurement techniques for homogeneous bulk samples, layered materials, and protruding cavity samples are discussed. Ultimately, a comprehensive overview of a steady state thermal characterization technique is provided, evaluating the accuracy of sample measurements with thermal resistances well below state of the art setups. Accurate characterization of materials used in heat removal applications, such as electronic packaging, will enable more efficient designs and ultimately contribute to energy savings.« less

Influence of survey strategy and interpolation model on DEM quality

NASA Astrophysics Data System (ADS)

Heritage, George L.; Milan, David J.; Large, Andrew R. G.; Fuller, Ian C.

2009-11-01

Accurate characterisation of morphology is critical to many studies in the field of geomorphology, particularly those dealing with changes over time. Digital elevation models (DEMs) are commonly used to represent morphology in three dimensions. The quality of the DEM is largely a function of the accuracy of individual survey points, field survey strategy, and the method of interpolation. Recommendations concerning field survey strategy and appropriate methods of interpolation are currently lacking. Furthermore, the majority of studies to date consider error to be uniform across a surface. This study quantifies survey strategy and interpolation error for a gravel bar on the River Nent, Blagill, Cumbria, UK. Five sampling strategies were compared: (i) cross section; (ii) bar outline only; (iii) bar and chute outline; (iv) bar and chute outline with spot heights; and (v) aerial LiDAR equivalent, derived from degraded terrestrial laser scan (TLS) data. Digital Elevation Models were then produced using five different common interpolation algorithms. Each resultant DEM was differentiated from a terrestrial laser scan of the gravel bar surface in order to define the spatial distribution of vertical and volumetric error. Overall triangulation with linear interpolation (TIN) or point kriging appeared to provide the best interpolators for the bar surface. Lowest error on average was found for the simulated aerial LiDAR survey strategy, regardless of interpolation technique. However, comparably low errors were also found for the bar-chute-spot sampling strategy when TINs or point kriging was used as the interpolator. The magnitude of the errors between survey strategy exceeded those found between interpolation technique for a specific survey strategy. Strong relationships between local surface topographic variation (as defined by the standard deviation of vertical elevations in a 0.2-m diameter moving window), and DEM errors were also found, with much greater errors found at slope breaks such as bank edges. A series of curves are presented that demonstrate these relationships for each interpolation and survey strategy. The simulated aerial LiDAR data set displayed the lowest errors across the flatter surfaces; however, sharp slope breaks are better modelled by the morphologically based survey strategy. The curves presented have general application to spatially distributed data of river beds and may be applied to standard deviation grids to predict spatial error within a surface, depending upon sampling strategy and interpolation algorithm.

Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

NASA Astrophysics Data System (ADS)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both heteroscedastic and homoscedastic data. Conversely, samples related to different methods produce discrepant results, due to the presence of (still undetected) systematic errors, which implies no definitive statement can be made at present. A comparison is also made between different expressions of regression line slope and intercept variance estimators, where fractional discrepancies are found to be not exceeding a few percent, which grows up to about 20% in the presence of large dispersion data. An extension of the formalism to structural models is left to a forthcoming paper.

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy.

PubMed

Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas

2013-01-01

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(-1) for mineral soils and a root mean square error of 50 g C kg(-1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.

New Insights into Handling Missing Values in Environmental Epidemiological Studies

PubMed Central

Roda, Célina; Nicolis, Ioannis; Momas, Isabelle; Guihenneuc, Chantal

2014-01-01

Missing data are unavoidable in environmental epidemiologic surveys. The aim of this study was to compare methods for handling large amounts of missing values: omission of missing values, single and multiple imputations (through linear regression or partial least squares regression), and a fully Bayesian approach. These methods were applied to the PARIS birth cohort, where indoor domestic pollutant measurements were performed in a random sample of babies' dwellings. A simulation study was conducted to assess performances of different approaches with a high proportion of missing values (from 50% to 95%). Different simulation scenarios were carried out, controlling the true value of the association (odds ratio of 1.0, 1.2, and 1.4), and varying the health outcome prevalence. When a large amount of data is missing, omitting these missing data reduced statistical power and inflated standard errors, which affected the significance of the association. Single imputation underestimated the variability, and considerably increased risk of type I error. All approaches were conservative, except the Bayesian joint model. In the case of a common health outcome, the fully Bayesian approach is the most efficient approach (low root mean square error, reasonable type I error, and high statistical power). Nevertheless for a less prevalent event, the type I error is increased and the statistical power is reduced. The estimated posterior distribution of the OR is useful to refine the conclusion. Among the methods handling missing values, no approach is absolutely the best but when usual approaches (e.g. single imputation) are not sufficient, joint modelling approach of missing process and health association is more efficient when large amounts of data are missing. PMID:25226278

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy

PubMed Central

Stevens, Antoine; Nocita, Marco; Tóth, Gergely; Montanarella, Luca; van Wesemael, Bas

2013-01-01

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg−1 for mineral soils and a root mean square error of 50 g C kg−1 for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation. PMID:23840459

Estimating accuracy of land-cover composition from two-stage cluster sampling

USGS Publications Warehouse

Stehman, S.V.; Wickham, J.D.; Fattorini, L.; Wade, T.D.; Baffetta, F.; Smith, J.H.

2009-01-01

Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), root mean square error (RMSE), and correlation (CORR) to quantify accuracy of land-cover composition for a general two-stage cluster sampling design, and for the special case of simple random sampling without replacement (SRSWOR) at each stage. The bias of the estimators for the two-stage SRSWOR design is evaluated via a simulation study. The estimators of RMSE and CORR have small bias except when sample size is small and the land-cover class is rare. The estimator of MAD is biased for both rare and common land-cover classes except when sample size is large. A general recommendation is that rare land-cover classes require large sample sizes to ensure that the accuracy estimators have small bias. ?? 2009 Elsevier Inc.

How to test validity in orthodontic research: a mixed dentition analysis example.

PubMed

Donatelli, Richard E; Lee, Shin-Jae

2015-02-01

The data used to test the validity of a prediction method should be different from the data used to generate the prediction model. In this study, we explored whether an independent data set is mandatory for testing the validity of a new prediction method and how validity can be tested without independent new data. Several validation methods were compared in an example using the data from a mixed dentition analysis with a regression model. The validation errors of real mixed dentition analysis data and simulation data were analyzed for increasingly large data sets. The validation results of both the real and the simulation studies demonstrated that the leave-1-out cross-validation method had the smallest errors. The largest errors occurred in the traditional simple validation method. The differences between the validation methods diminished as the sample size increased. The leave-1-out cross-validation method seems to be an optimal validation method for improving the prediction accuracy in a data set with limited sample sizes. Copyright © 2015 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Mapping ecological systems with a random foret model: tradeoffs between errors and bias

Treesearch

Emilie Grossmann; Janet Ohmann; James Kagan; Heather May; Matthew Gregory

2010-01-01

New methods for predictive vegetation mapping allow improved estimations of plant community composition across large regions. Random Forest (RF) models limit over-fitting problems of other methods, and are known for making accurate classification predictions from noisy, nonnormal data, but can be biased when plot samples are unbalanced. We developed two contrasting...

Limits on the Accuracy of Linking. Research Report. ETS RR-10-22

ERIC Educational Resources Information Center

Haberman, Shelby J.

2010-01-01

Sampling errors limit the accuracy with which forms can be linked. Limitations on accuracy are especially important in testing programs in which a very large number of forms are employed. Standard inequalities in mathematical statistics may be used to establish lower bounds on the achievable inking accuracy. To illustrate results, a variety of…

Profile-likelihood Confidence Intervals in Item Response Theory Models.

PubMed

Chalmers, R Philip; Pek, Jolynn; Liu, Yang

2017-01-01

Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

NASA Technical Reports Server (NTRS)

Fisher, Brad; Wolff, David B.

2010-01-01

Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

Performance of the all-digital data-transition tracking loop in the advanced receiver

NASA Astrophysics Data System (ADS)

Cheng, U.; Hinedi, S.

1989-11-01

The performance of the all-digital data-transition tracking loop (DTTL) with coherent or noncoherent sampling is described. The effects of few samples per symbol and of noncommensurate sampling rates and symbol rates are addressed and analyzed. Their impacts on the loop phase-error variance and the mean time to lose lock (MTLL) are quantified through computer simulations. The analysis and preliminary simulations indicate that with three to four samples per symbol, the DTTL can track with negligible jitter because of the presence of earth Doppler rate. Furthermore, the MTLL is also expected to be large engough to maintain lock over a Deep Space Network track.

SU-F-J-193: Efficient Dose Extinction Method for Water Equivalent Path Length (WEPL) of Real Tissue Samples for Validation of CT HU to Stopping Power Conversion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, R; Baer, E; Jee, K

Purpose: For proton therapy, an accurate model of CT HU to relative stopping power (RSP) conversion is essential. In current practice, validation of these models relies solely on measurements of tissue substitutes with standard compositions. Validation based on real tissue samples would be much more direct and can address variations between patients. This study intends to develop an efficient and accurate system based on the concept of dose extinction to measure WEPL and retrieve RSP in biological tissue in large number of types. Methods: A broad AP proton beam delivering a spread out Bragg peak (SOBP) is used to irradiatemore » the samples with a Matrixx detector positioned immediately below. A water tank was placed on top of the samples, with the water level controllable in sub-millimeter by a remotely controlled dosing pump. While gradually lowering the water level with beam on, the transmission dose was recorded at 1 frame/sec. The WEPL were determined as the difference between the known beam range of the delivered SOBP (80%) and the water level corresponding to 80% of measured dose profiles in time. A Gammex 467 phantom was used to test the system and various types of biological tissue was measured. Results: RSP for all Gammex inserts, expect the one made with lung-450 material (<2% error), were determined within ±0.5% error. Depends on the WEPL of investigated phantom, a measurement takes around 10 min, which can be accelerated by a faster pump. Conclusion: Based on the concept of dose extinction, a system was explored to measure WEPL efficiently and accurately for a large number of samples. This allows the validation of CT HU to stopping power conversions based on large number of samples and real tissues. It also allows the assessment of beam uncertainties due to variations over patients, which issue has never been sufficiently studied before.« less

Prevalence and types of preanalytical error in hematology laboratory of a tertiary care hospital in South India.

PubMed

Arul, Pitchaikaran; Pushparaj, Magesh; Pandian, Kanmani; Chennimalai, Lingasamy; Rajendran, Karthika; Selvaraj, Eniya; Masilamani, Suresh

2018-01-01

An important component of laboratory medicine is preanalytical phase. Since laboratory report plays a major role in patient management, more importance should be given to the quality of laboratory tests. The present study was undertaken to find the prevalence and types of preanalytical errors at a tertiary care hospital in South India. In this cross-sectional study, a total of 118,732 samples ([62,474 outpatient department [OPD] and 56,258 inpatient department [IPD]) were received in hematology laboratory. These samples were analyzed for preanalytical errors such as misidentification, incorrect vials, inadequate samples, clotted samples, diluted samples, and hemolyzed samples. The overall prevalence of preanalytical errors found was 513 samples, which is 0.43% of the total number of samples received. The most common preanalytical error observed was inadequate samples followed by clotted samples. Overall frequencies (both OPD and IPD) of preanalytical errors such as misidentification, incorrect vials, inadequate samples, clotted samples, diluted samples, and hemolyzed samples were 0.02%, 0.05%, 0.2%, 0.12%, 0.02%, and 0.03%, respectively. The present study concluded that incorrect phlebotomy techniques due to lack of awareness is the main reason for preanalytical errors. This can be avoided by proper communication and coordination between laboratory and wards, proper training and continuing medical education programs for laboratory and paramedical staffs, and knowledge of the intervening factors that can influence laboratory results.

Bias correction for selecting the minimal-error classifier from many machine learning models.

PubMed

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Eigenvalue sensitivity of sampled time systems operating in closed loop

NASA Astrophysics Data System (ADS)

Bernal, Dionisio

2018-05-01

The use of feedback to create closed-loop eigenstructures with high sensitivity has received some attention in the Structural Health Monitoring field. Although practical implementation is necessarily digital, and thus in sampled time, work thus far has center on the continuous time framework, both in design and in checking performance. It is shown in this paper that the performance in discrete time, at typical sampling rates, can differ notably from that anticipated in the continuous time formulation and that discrepancies can be particularly large on the real part of the eigenvalue sensitivities; a consequence being important error on the (linear estimate) of the level of damage at which closed-loop stability is lost. As one anticipates, explicit consideration of the sampling rate poses no special difficulties in the closed-loop eigenstructure design and the relevant expressions are developed in the paper, including a formula for the efficient evaluation of the derivative of the matrix exponential based on the theory of complex perturbations. The paper presents an easily reproduced numerical example showing the level of error that can result when the discrete time implementation of the controller is not considered.

A molecular simulation protocol to avoid sampling redundancy and discover new states.

PubMed

Bacci, Marco; Vitalis, Andreas; Caflisch, Amedeo

2015-05-01

For biomacromolecules or their assemblies, experimental knowledge is often restricted to specific states. Ambiguity pervades simulations of these complex systems because there is no prior knowledge of relevant phase space domains, and sampling recurrence is difficult to achieve. In molecular dynamics methods, ruggedness of the free energy surface exacerbates this problem by slowing down the unbiased exploration of phase space. Sampling is inefficient if dwell times in metastable states are large. We suggest a heuristic algorithm to terminate and reseed trajectories run in multiple copies in parallel. It uses a recent method to order snapshots, which provides notions of "interesting" and "unique" for individual simulations. We define criteria to guide the reseeding of runs from more "interesting" points if they sample overlapping regions of phase space. Using a pedagogical example and an α-helical peptide, the approach is demonstrated to amplify the rate of exploration of phase space and to discover metastable states not found by conventional sampling schemes. Evidence is provided that accurate kinetics and pathways can be extracted from the simulations. The method, termed PIGS for Progress Index Guided Sampling, proceeds in unsupervised fashion, is scalable, and benefits synergistically from larger numbers of replicas. Results confirm that the underlying ideas are appropriate and sufficient to enhance sampling. In molecular simulations, errors caused by not exploring relevant domains in phase space are always unquantifiable and can be arbitrarily large. Our protocol adds to the toolkit available to researchers in reducing these types of errors. This article is part of a Special Issue entitled "Recent developments of molecular dynamics". Copyright © 2014 Elsevier B.V. All rights reserved.

Sampling Errors in Monthly Rainfall Totals for TRMM and SSM/I, Based on Statistics of Retrieved Rain Rates and Simple Models

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Kundu, Prasun K.; Einaudi, Franco (Technical Monitor)

2000-01-01

Estimates from TRMM satellite data of monthly total rainfall over an area are subject to substantial sampling errors due to the limited number of visits to the area by the satellite during the month. Quantitative comparisons of TRMM averages with data collected by other satellites and by ground-based systems require some estimate of the size of this sampling error. A method of estimating this sampling error based on the actual statistics of the TRMM observations and on some modeling work has been developed. "Sampling error" in TRMM monthly averages is defined here relative to the monthly total a hypothetical satellite permanently stationed above the area would have reported. "Sampling error" therefore includes contributions from the random and systematic errors introduced by the satellite remote sensing system. As part of our long-term goal of providing error estimates for each grid point accessible to the TRMM instruments, sampling error estimates for TRMM based on rain retrievals from TRMM microwave (TMI) data are compared for different times of the year and different oceanic areas (to minimize changes in the statistics due to algorithmic differences over land and ocean). Changes in sampling error estimates due to changes in rain statistics due 1) to evolution of the official algorithms used to process the data, and 2) differences from other remote sensing systems such as the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I), are analyzed.

Efficient Time-Domain Imaging Processing for One-Stationary Bistatic Forward-Looking SAR Including Motion Errors

PubMed Central

Xie, Hongtu; Shi, Shaoying; Xiao, Hui; Xie, Chao; Wang, Feng; Fang, Qunle

2016-01-01

With the rapid development of the one-stationary bistatic forward-looking synthetic aperture radar (OS-BFSAR) technology, the huge amount of the remote sensing data presents challenges for real-time imaging processing. In this paper, an efficient time-domain algorithm (ETDA) considering the motion errors for the OS-BFSAR imaging processing, is presented. This method can not only precisely handle the large spatial variances, serious range-azimuth coupling and motion errors, but can also greatly improve the imaging efficiency compared with the direct time-domain algorithm (DTDA). Besides, it represents the subimages on polar grids in the ground plane instead of the slant-range plane, and derives the sampling requirements considering motion errors for the polar grids to offer a near-optimum tradeoff between the imaging precision and efficiency. First, OS-BFSAR imaging geometry is built, and the DTDA for the OS-BFSAR imaging is provided. Second, the polar grids of subimages are defined, and the subaperture imaging in the ETDA is derived. The sampling requirements for polar grids are derived from the point of view of the bandwidth. Finally, the implementation and computational load of the proposed ETDA are analyzed. Experimental results based on simulated and measured data validate that the proposed ETDA outperforms the DTDA in terms of the efficiency improvement. PMID:27845757

Reducing Individual Variation for fMRI Studies in Children by Minimizing Template Related Errors

PubMed Central

Weng, Jian; Dong, Shanshan; He, Hongjian; Chen, Feiyan; Peng, Xiaogang

2015-01-01

Spatial normalization is an essential process for group comparisons in functional MRI studies. In practice, there is a risk of normalization errors particularly in studies involving children, seniors or diseased populations and in regions with high individual variation. One way to minimize normalization errors is to create a study-specific template based on a large sample size. However, studies with a large sample size are not always feasible, particularly for children studies. The performance of templates with a small sample size has not been evaluated in fMRI studies in children. In the current study, this issue was encountered in a working memory task with 29 children in two groups. We compared the performance of different templates: a study-specific template created by the experimental population, a Chinese children template and the widely used adult MNI template. We observed distinct differences in the right orbitofrontal region among the three templates in between-group comparisons. The study-specific template and the Chinese children template were more sensitive for the detection of between-group differences in the orbitofrontal cortex than the MNI template. Proper templates could effectively reduce individual variation. Further analysis revealed a correlation between the BOLD contrast size and the norm index of the affine transformation matrix, i.e., the SFN, which characterizes the difference between a template and a native image and differs significantly across subjects. Thereby, we proposed and tested another method to reduce individual variation that included the SFN as a covariate in group-wise statistics. This correction exhibits outstanding performance in enhancing detection power in group-level tests. A training effect of abacus-based mental calculation was also demonstrated, with significantly elevated activation in the right orbitofrontal region that correlated with behavioral response time across subjects in the trained group. PMID:26207985

Power of one nonclean qubit

NASA Astrophysics Data System (ADS)

Morimae, Tomoyuki; Fujii, Keisuke; Nishimura, Harumichi

2017-04-01

The one-clean qubit model (or the DQC1 model) is a restricted model of quantum computing where only a single qubit of the initial state is pure and others are maximally mixed. Although the model is not universal, it can efficiently solve several problems whose classical efficient solutions are not known. Furthermore, it was recently shown that if the one-clean qubit model is classically efficiently simulated, the polynomial hierarchy collapses to the second level. A disadvantage of the one-clean qubit model is, however, that the clean qubit is too clean: for example, in realistic NMR experiments, polarizations are not high enough to have the perfectly pure qubit. In this paper, we consider a more realistic one-clean qubit model, where the clean qubit is not clean, but depolarized. We first show that, for any polarization, a multiplicative-error calculation of the output probability distribution of the model is possible in a classical polynomial time if we take an appropriately large multiplicative error. The result is in strong contrast with that of the ideal one-clean qubit model where the classical efficient multiplicative-error calculation (or even the sampling) with the same amount of error causes the collapse of the polynomial hierarchy. We next show that, for any polarization lower-bounded by an inverse polynomial, a classical efficient sampling (in terms of a sufficiently small multiplicative error or an exponentially small additive error) of the output probability distribution of the model is impossible unless BQP (bounded error quantum polynomial time) is contained in the second level of the polynomial hierarchy, which suggests the hardness of the classical efficient simulation of the one nonclean qubit model.

Assessing the Impact of Pre-gpm Microwave Precipitation Observations in the Goddard WRF Ensemble Data Assimilation System

NASA Technical Reports Server (NTRS)

Chambon, Philippe; Zhang, Sara Q.; Hou, Arthur Y.; Zupanski, Milija; Cheung, Samson

2013-01-01

The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.

Physical Validation of TRMM TMI and PR Monthly Rain Products Over Oklahoma

NASA Technical Reports Server (NTRS)

Fisher, Brad L.

2004-01-01

The Tropical Rainfall Measuring Mission (TRMM) provides monthly rainfall estimates using data collected by the TRMM satellite. These estimates cover a substantial fraction of the earth's surface. The physical validation of TRMM estimates involves corroborating the accuracy of spaceborne estimates of areal rainfall by inferring errors and biases from ground-based rain estimates. The TRMM error budget consists of two major sources of error: retrieval and sampling. Sampling errors are intrinsic to the process of estimating monthly rainfall and occur because the satellite extrapolates monthly rainfall from a small subset of measurements collected only during satellite overpasses. Retrieval errors, on the other hand, are related to the process of collecting measurements while the satellite is overhead. One of the big challenges confronting the TRMM validation effort is how to best estimate these two main components of the TRMM error budget, which are not easily decoupled. This four-year study computed bulk sampling and retrieval errors for the TRMM microwave imager (TMI) and the precipitation radar (PR) by applying a technique that sub-samples gauge data at TRMM overpass times. Gridded monthly rain estimates are then computed from the monthly bulk statistics of the collected samples, providing a sensor-dependent gauge rain estimate that is assumed to include a TRMM equivalent sampling error. The sub-sampled gauge rain estimates are then used in conjunction with the monthly satellite and gauge (without sub- sampling) estimates to decouple retrieval and sampling errors. The computed mean sampling errors for the TMI and PR were 5.9% and 7.796, respectively, in good agreement with theoretical predictions. The PR year-to-year retrieval biases exceeded corresponding TMI biases, but it was found that these differences were partially due to negative TMI biases during cold months and positive TMI biases during warm months.

Designing robust watermark barcodes for multiplex long-read sequencing.

PubMed

Ezpeleta, Joaquín; Krsticevic, Flavia J; Bulacio, Pilar; Tapia, Elizabeth

2017-03-15

To attain acceptable sample misassignment rates, current approaches to multiplex single-molecule real-time sequencing require upstream quality improvement, which is obtained from multiple passes over the sequenced insert and significantly reduces the effective read length. In order to fully exploit the raw read length on multiplex applications, robust barcodes capable of dealing with the full single-pass error rates are needed. We present a method for designing sequencing barcodes that can withstand a large number of insertion, deletion and substitution errors and are suitable for use in multiplex single-molecule real-time sequencing. The manuscript focuses on the design of barcodes for full-length single-pass reads, impaired by challenging error rates in the order of 11%. The proposed barcodes can multiplex hundreds or thousands of samples while achieving sample misassignment probabilities as low as 10-7 under the above conditions, and are designed to be compatible with chemical constraints imposed by the sequencing process. Software tools for constructing watermark barcode sets and demultiplexing barcoded reads, together with example sets of barcodes and synthetic barcoded reads, are freely available at www.cifasis-conicet.gov.ar/ezpeleta/NS-watermark . ezpeleta@cifasis-conicet.gov.ar. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Implementing a laboratory automation system: experience of a large clinical laboratory.

PubMed

Lam, Choong Weng; Jacob, Edward

2012-02-01

Laboratories today face increasing pressure to automate their operations as they are challenged by a continuing increase in workload, need to reduce expenditure, and difficulties in recruitment of experienced technical staff. Was the implementation of a laboratory automation system (LAS) in the Clinical Biochemistry Laboratory at Singapore General Hospital successful? There is no simple answer, so the following topics comparing and contrasting pre- and post-LAS have been explored: turnaround time (TAT), laboratory errors, and staff satisfaction. The benefits and limitations of LAS from the laboratory experience were also reviewed. The mean TAT for both stat and routine samples decreased post-LAS (30% and 13.4%, respectively). In the 90th percentile TAT chart, a 29% reduction was seen in the processing of stat samples on the LAS. However, no significant difference in the 90th percentile TAT was observed with routine samples. It was surprising to note that laboratory errors increased post-LAS. Considerable effort was needed to overcome the initial difficulties associated with adjusting to a new system, new software, and new working procedures. Although some of the known advantages and limitations of LAS have been validated, the claimed benefits such as improvements in TAT, laboratory errors, and staff morale were not evident in the initial months.

Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample.

PubMed

Xu, Stanley; Clarke, Christina L; Newcomer, Sophia R; Daley, Matthew F; Glanz, Jason M

2018-05-16

Vaccine safety studies are often electronic health record (EHR)-based observational studies. These studies often face significant methodological challenges, including confounding and misclassification of adverse event. Vaccine safety researchers use self-controlled case series (SCCS) study design to handle confounding effect and employ medical chart review to ascertain cases that are identified using EHR data. However, for common adverse events, limited resources often make it impossible to adjudicate all adverse events observed in electronic data. In this paper, we considered four approaches for analyzing SCCS data with confirmation rates estimated from an internal validation sample: (1) observed cases, (2) confirmed cases only, (3) known confirmation rate, and (4) multiple imputation (MI). We conducted a simulation study to evaluate these four approaches using type I error rates, percent bias, and empirical power. Our simulation results suggest that when misclassification of adverse events is present, approaches such as observed cases, confirmed case only, and known confirmation rate may inflate the type I error, yield biased point estimates, and affect statistical power. The multiple imputation approach considers the uncertainty of estimated confirmation rates from an internal validation sample, yields a proper type I error rate, largely unbiased point estimate, proper variance estimate, and statistical power. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Laboratory and airborne techniques for measuring fluoresence of natural surfaces

NASA Technical Reports Server (NTRS)

Stoertz, G. E.; Hemphill, W. R.

1972-01-01

Techniques are described for obtaining fluorescence spectra from samples of natural surfaces that can be used to predict spectral regions in which these surfaces would emit solar-stimulated or laser-stimulated fluorescence detectable by remote sensor. Scattered or reflected stray light caused large errors in spectrofluorometer analysis or natural sample surfaces. Most spurious light components can be eliminated by recording successive fluorescence spectra for each sample, using identical instrument settings, first with an appropriate glass or gelatin filter on the excitation side of the sample, and subsequently with the same filter on the emission side of the sample. This technique appears more accurate than any alternative technique for testing the fluorescence of natural surfaces.

Analysis of surface-water data network in Kansas for effectiveness in providing regional streamflow information; with a section on theory and application of generalized least squares

USGS Publications Warehouse

Medina, K.D.; Tasker, Gary D.

1987-01-01

This report documents the results of an analysis of the surface-water data network in Kansas for its effectiveness in providing regional streamflow information. The network was analyzed using generalized least squares regression. The correlation and time-sampling error of the streamflow characteristic are considered in the generalized least squares method. Unregulated medium-, low-, and high-flow characteristics were selected to be representative of the regional information that can be obtained from streamflow-gaging-station records for use in evaluating the effectiveness of continuing the present network stations, discontinuing some stations, and (or) adding new stations. The analysis used streamflow records for all currently operated stations that were not affected by regulation and for discontinued stations for which unregulated flow characteristics, as well as physical and climatic characteristics, were available. The State was divided into three network areas, western, northeastern, and southeastern Kansas, and analysis was made for the three streamflow characteristics in each area, using three planning horizons. The analysis showed that the maximum reduction of sampling mean-square error for each cost level could be obtained by adding new stations and discontinuing some current network stations. Large reductions in sampling mean-square error for low-flow information could be achieved in all three network areas, the reduction in western Kansas being the most dramatic. The addition of new stations would be most beneficial for mean-flow information in western Kansas. The reduction of sampling mean-square error for high-flow information would benefit most from the addition of new stations in western Kansas. Southeastern Kansas showed the smallest error reduction in high-flow information. A comparison among all three network areas indicated that funding resources could be most effectively used by discontinuing more stations in northeastern and southeastern Kansas and establishing more new stations in western Kansas.

Analysis of surface-water data network in Kansas for effectiveness in providing regional streamflow information

USGS Publications Warehouse

Medina, K.D.; Tasker, Gary D.

1985-01-01

The surface water data network in Kansas was analyzed using generalized least squares regression for its effectiveness in providing regional streamflow information. The correlation and time-sampling error of the streamflow characteristic are considered in the generalized least squares method. Unregulated medium-flow, low-flow and high-flow characteristics were selected to be representative of the regional information that can be obtained from streamflow gaging station records for use in evaluating the effectiveness of continuing the present network stations, discontinuing some stations; and/or adding new stations. The analysis used streamflow records for all currently operated stations that were not affected by regulation and discontinued stations for which unregulated flow characteristics , as well as physical and climatic characteristics, were available. The state was divided into three network areas, western, northeastern, and southeastern Kansas, and analysis was made for three streamflow characteristics in each area, using three planning horizons. The analysis showed that the maximum reduction of sampling mean square error for each cost level could be obtained by adding new stations and discontinuing some of the present network stations. Large reductions in sampling mean square error for low-flow information could be accomplished in all three network areas, with western Kansas having the most dramatic reduction. The addition of new stations would be most beneficial for man- flow information in western Kansas, and to lesser degrees in the other two areas. The reduction of sampling mean square error for high-flow information would benefit most from the addition of new stations in western Kansas, and the effect diminishes to lesser degrees in the other two areas. Southeastern Kansas showed the smallest error reduction in high-flow information. A comparison among all three network areas indicated that funding resources could be most effectively used by discontinuing more stations in northeastern and southeastern Kansas and establishing more new stations in western Kansas. (Author 's abstract)

Preanalytical Errors in Hematology Laboratory- an Avoidable Incompetence.

PubMed

HarsimranKaur, Vikram Narang; Selhi, Pavneet Kaur; Sood, Neena; Singh, Aminder

2016-01-01

Quality assurance in the hematology laboratory is a must to ensure laboratory users of reliable test results with high degree of precision and accuracy. Even after so many advances in hematology laboratory practice, pre-analytical errors remain a challenge for practicing pathologists. This study was undertaken with an objective to evaluate the types and frequency of preanalytical errors in hematology laboratory of our center. All the samples received in the Hematology Laboratory of Dayanand Medical College and Hospital, Ludhiana, India over a period of one year (July 2013-July 2014) were included in the study and preanalytical variables like clotted samples, quantity not sufficient, wrong sample, without label, wrong label were studied. Of 471,006 samples received in the laboratory, preanalytical errors, as per the above mentioned categories was found in 1802 samples. The most common error was clotted samples (1332 samples, 0.28% of the total samples) followed by quantity not sufficient (328 sample, 0.06%), wrong sample (96 samples, 0.02%), without label (24 samples, 0.005%) and wrong label (22 samples, 0.005%). Preanalytical errors are frequent in laboratories and can be corrected by regular analysis of the variables involved. Rectification can be done by regular education of the staff.

Evaluating mixed samples as a source of error in non-invasive genetic studies using microsatellites

USGS Publications Warehouse

Roon, David A.; Thomas, M.E.; Kendall, K.C.; Waits, L.P.

2005-01-01

The use of noninvasive genetic sampling (NGS) for surveying wild populations is increasing rapidly. Currently, only a limited number of studies have evaluated potential biases associated with NGS. This paper evaluates the potential errors associated with analysing mixed samples drawn from multiple animals. Most NGS studies assume that mixed samples will be identified and removed during the genotyping process. We evaluated this assumption by creating 128 mixed samples of extracted DNA from brown bear (Ursus arctos) hair samples. These mixed samples were genotyped and screened for errors at six microsatellite loci according to protocols consistent with those used in other NGS studies. Five mixed samples produced acceptable genotypes after the first screening. However, all mixed samples produced multiple alleles at one or more loci, amplified as only one of the source samples, or yielded inconsistent electropherograms by the final stage of the error-checking process. These processes could potentially reduce the number of individuals observed in NGS studies, but errors should be conservative within demographic estimates. Researchers should be aware of the potential for mixed samples and carefully design gel analysis criteria and error checking protocols to detect mixed samples.

Factors affecting the sticking of insects on modified aircraft wings

NASA Technical Reports Server (NTRS)

Yi, O.; Chan, R.; Eiss, N. S.; Pingali, U.; Wightman, J. P.

1988-01-01

The adhesion of insects to aircraft wings is studied. Insects were collected in road tests in past studies and a large experimental error was introduced caused by the variability of insect flux. The presence of such errors has been detected by studying the insect distribution across an aluminum-strip covered half-cylinder mounted on the top of a car. After a nonuniform insect distribution (insect flux) was found from three road tests, a new arrangement of samples was developed. The feasibility of coating aircraft wing surfaces with polymers to reduce the number of insects sticking onto the surfaces was studied using fluorocarbon elastomers, styrene butadiene rubbers, and Teflon.

Sodium in weak G-band giants

NASA Technical Reports Server (NTRS)

Drake, Jeremy J.; Lambert, David L.

1994-01-01

Sodium abundances have been determined for eight weak G-band giants whose atmospheres are greatly enriched with products of the CN-cycling H-burning reactions. Systematic errors are minimized by comparing the weak G-band giants to a sample of similar but normal giants. If, further, Ca is selected as a reference element, model atmosphere-related errors should largely be removed. For the weak-G-band stars (Na/Ca) = 0.16 +/- 0.01, which is just possibly greater than the result (Na/Ca) = 0.10 /- 0.03 from the normal giants. This result demonstrates that the atmospheres of the weak G-band giants are not seriously contaminated with products of ON cycling.

Perception of Community Pharmacists towards Dispensing Errors in Community Pharmacy Setting in Gondar Town, Northwest Ethiopia

PubMed Central

2017-01-01

Background Dispensing errors are inevitable occurrences in community pharmacies across the world. Objective This study aimed to identify the community pharmacists' perception towards dispensing errors in the community pharmacies in Gondar town, Northwest Ethiopia. Methods A cross-sectional study was conducted among 47 community pharmacists selected through convenience sampling. Data were analyzed using SPSS version 20. Descriptive statistics, Mann–Whitney U test, and Pearson's Chi-square test of independence were conducted with P ≤ 0.05 considered statistically significant. Result The majority of respondents were in the 23–28-year age group (N = 26, 55.3%) and with at least B.Pharm degree (N = 25, 53.2%). Poor prescription handwriting and similar/confusing names were perceived to be the main contributing factors while all the strategies and types of dispensing errors were highly acknowledged by the respondents. Group differences (P < 0.05) in opinions were largely due to educational level and age. Conclusion Dispensing errors were associated with prescribing quality and design of dispensary as well as dispensing procedures. Opinion differences relate to age and educational status of the respondents. PMID:28612023

Perception of Community Pharmacists towards Dispensing Errors in Community Pharmacy Setting in Gondar Town, Northwest Ethiopia.

PubMed

Asmelashe Gelayee, Dessalegn; Binega Mekonnen, Gashaw

2017-01-01

Dispensing errors are inevitable occurrences in community pharmacies across the world. This study aimed to identify the community pharmacists' perception towards dispensing errors in the community pharmacies in Gondar town, Northwest Ethiopia. A cross-sectional study was conducted among 47 community pharmacists selected through convenience sampling. Data were analyzed using SPSS version 20. Descriptive statistics, Mann-Whitney U test, and Pearson's Chi-square test of independence were conducted with P ≤ 0.05 considered statistically significant. The majority of respondents were in the 23-28-year age group ( N = 26, 55.3%) and with at least B.Pharm degree ( N = 25, 53.2%). Poor prescription handwriting and similar/confusing names were perceived to be the main contributing factors while all the strategies and types of dispensing errors were highly acknowledged by the respondents. Group differences ( P < 0.05) in opinions were largely due to educational level and age. Dispensing errors were associated with prescribing quality and design of dispensary as well as dispensing procedures. Opinion differences relate to age and educational status of the respondents.

Reconstruction of regional mean temperature for East Asia since 1900s and its uncertainties

NASA Astrophysics Data System (ADS)

Hua, W.

2017-12-01

Regional average surface air temperature (SAT) is one of the key variables often used to investigate climate change. Unfortunately, because of the limited observations over East Asia, there were also some gaps in the observation data sampling for regional mean SAT analysis, which was important to estimate past climate change. In this study, the regional average temperature of East Asia since 1900s is calculated by the Empirical Orthogonal Function (EOF)-based optimal interpolation (OA) method with considering the data errors. The results show that our estimate is more precise and robust than the results from simple average, which provides a better way for past climate reconstruction. In addition to the reconstructed regional average SAT anomaly time series, we also estimated uncertainties of reconstruction. The root mean square error (RMSE) results show that the the error decreases with respect to time, and are not sufficiently large to alter the conclusions on the persist warming in East Asia during twenty-first century. Moreover, the test of influence of data error on reconstruction clearly shows the sensitivity of reconstruction to the size of the data error.

An error criterion for determining sampling rates in closed-loop control systems

NASA Technical Reports Server (NTRS)

Brecher, S. M.

1972-01-01

The determination of an error criterion which will give a sampling rate for adequate performance of linear, time-invariant closed-loop, discrete-data control systems was studied. The proper modelling of the closed-loop control system for characterization of the error behavior, and the determination of an absolute error definition for performance of the two commonly used holding devices are discussed. The definition of an adequate relative error criterion as a function of the sampling rate and the parameters characterizing the system is established along with the determination of sampling rates. The validity of the expressions for the sampling interval was confirmed by computer simulations. Their application solves the problem of making a first choice in the selection of sampling rates.

Lessons learned: wrong intraocular lens.

PubMed

Schein, Oliver D; Banta, James T; Chen, Teresa C; Pritzker, Scott; Schachat, Andrew P

2012-10-01

To report cases involving the placement of the wrong intraocular lens (IOL) at the time of cataract surgery where human error occurred. Retrospective small case series, convenience sample. Seven surgical cases. Institutional review of errors committed and subsequent improvements to clinical protocols. Lessons learned and changes in procedures adapted. The pathways to a wrong IOL are many but largely reflect some combination of poor surgical team communication, transcription error, lack of preoperative clarity in surgical planning or failure to match the patient, and IOL calculation sheet with 2 unique identifiers. Safety in surgery involving IOLs is enhanced both by strict procedures, such as an IOL-specific "time-out," and the fostering of a surgical team culture in which all members are encouraged to voice questions and concerns. Copyright © 2012 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Prevalence of Pre-Analytical Errors in Clinical Chemistry Diagnostic Labs in Sulaimani City of Iraqi Kurdistan

PubMed Central

2017-01-01

Background Laboratory testing is roughly divided into three phases: a pre-analytical phase, an analytical phase and a post-analytical phase. Most analytical errors have been attributed to the analytical phase. However, recent studies have shown that up to 70% of analytical errors reflect the pre-analytical phase. The pre-analytical phase comprises all processes from the time a laboratory request is made by a physician until the specimen is analyzed at the lab. Generally, the pre-analytical phase includes patient preparation, specimen transportation, specimen collection and storage. In the present study, we report the first comprehensive assessment of the frequency and types of pre-analytical errors at the Sulaimani diagnostic labs in Iraqi Kurdistan. Materials and Methods Over 2 months, 5500 venous blood samples were observed in 10 public diagnostic labs of Sulaimani City. The percentages of rejected samples and types of sample inappropriateness were evaluated. The percentage of each of the following pre-analytical errors were recorded: delay in sample transportation, clotted samples, expired reagents, hemolyzed samples, samples not on ice, incorrect sample identification, insufficient sample, tube broken in centrifuge, request procedure errors, sample mix-ups, communication conflicts, misinterpreted orders, lipemic samples, contaminated samples and missed physician’s request orders. The difference between the relative frequencies of errors observed in the hospitals considered was tested using a proportional Z test. In particular, the survey aimed to discover whether analytical errors were recorded and examine the types of platforms used in the selected diagnostic labs. Results The analysis showed a high prevalence of improper sample handling during the pre-analytical phase. In appropriate samples, the percentage error was as high as 39%. The major reasons for rejection were hemolyzed samples (9%), incorrect sample identification (8%) and clotted samples (6%). Most quality control schemes at Sulaimani hospitals focus only on the analytical phase, and none of the pre-analytical errors were recorded. Interestingly, none of the labs were internationally accredited; therefore, corrective actions are needed at these hospitals to ensure better health outcomes. Internal and External Quality Assessment Schemes (EQAS) for the pre-analytical phase at Sulaimani clinical laboratories should be implemented at public hospitals. Furthermore, lab personnel, particularly phlebotomists, need continuous training on the importance of sample quality to obtain accurate test results. PMID:28107395

Prevalence of Pre-Analytical Errors in Clinical Chemistry Diagnostic Labs in Sulaimani City of Iraqi Kurdistan.

PubMed

Najat, Dereen

2017-01-01

Laboratory testing is roughly divided into three phases: a pre-analytical phase, an analytical phase and a post-analytical phase. Most analytical errors have been attributed to the analytical phase. However, recent studies have shown that up to 70% of analytical errors reflect the pre-analytical phase. The pre-analytical phase comprises all processes from the time a laboratory request is made by a physician until the specimen is analyzed at the lab. Generally, the pre-analytical phase includes patient preparation, specimen transportation, specimen collection and storage. In the present study, we report the first comprehensive assessment of the frequency and types of pre-analytical errors at the Sulaimani diagnostic labs in Iraqi Kurdistan. Over 2 months, 5500 venous blood samples were observed in 10 public diagnostic labs of Sulaimani City. The percentages of rejected samples and types of sample inappropriateness were evaluated. The percentage of each of the following pre-analytical errors were recorded: delay in sample transportation, clotted samples, expired reagents, hemolyzed samples, samples not on ice, incorrect sample identification, insufficient sample, tube broken in centrifuge, request procedure errors, sample mix-ups, communication conflicts, misinterpreted orders, lipemic samples, contaminated samples and missed physician's request orders. The difference between the relative frequencies of errors observed in the hospitals considered was tested using a proportional Z test. In particular, the survey aimed to discover whether analytical errors were recorded and examine the types of platforms used in the selected diagnostic labs. The analysis showed a high prevalence of improper sample handling during the pre-analytical phase. In appropriate samples, the percentage error was as high as 39%. The major reasons for rejection were hemolyzed samples (9%), incorrect sample identification (8%) and clotted samples (6%). Most quality control schemes at Sulaimani hospitals focus only on the analytical phase, and none of the pre-analytical errors were recorded. Interestingly, none of the labs were internationally accredited; therefore, corrective actions are needed at these hospitals to ensure better health outcomes. Internal and External Quality Assessment Schemes (EQAS) for the pre-analytical phase at Sulaimani clinical laboratories should be implemented at public hospitals. Furthermore, lab personnel, particularly phlebotomists, need continuous training on the importance of sample quality to obtain accurate test results.

Ultra-deep mutant spectrum profiling: improving sequencing accuracy using overlapping read pairs.

PubMed

Chen-Harris, Haiyin; Borucki, Monica K; Torres, Clinton; Slezak, Tom R; Allen, Jonathan E

2013-02-12

High throughput sequencing is beginning to make a transformative impact in the area of viral evolution. Deep sequencing has the potential to reveal the mutant spectrum within a viral sample at high resolution, thus enabling the close examination of viral mutational dynamics both within- and between-hosts. The challenge however, is to accurately model the errors in the sequencing data and differentiate real viral mutations, particularly those that exist at low frequencies, from sequencing errors. We demonstrate that overlapping read pairs (ORP) -- generated by combining short fragment sequencing libraries and longer sequencing reads -- significantly reduce sequencing error rates and improve rare variant detection accuracy. Using this sequencing protocol and an error model optimized for variant detection, we are able to capture a large number of genetic mutations present within a viral population at ultra-low frequency levels (<0.05%). Our rare variant detection strategies have important implications beyond viral evolution and can be applied to any basic and clinical research area that requires the identification of rare mutations.

Accounting for Relatedness in Family Based Genetic Association Studies

PubMed Central

McArdle, P.F.; O’Connell, J.R.; Pollin, T.I.; Baumgarten, M.; Shuldiner, A.R.; Peyser, P.A.; Mitchell, B.D.

2007-01-01

Objective Assess the differences in point estimates, power and type 1 error rates when accounting for and ignoring family structure in genetic tests of association. Methods We compare by simulation the performance of analytic models using variance components to account for family structure and regression models that ignore relatedness for a range of possible family based study designs (i.e., sib pairs vs. large sibships vs. nuclear families vs. extended families). Results Our analyses indicate that effect size estimates and power are not significantly affected by ignoring family structure. Type 1 error rates increase when family structure is ignored, as density of family structures increases, and as trait heritability increases. For discrete traits with moderate levels of heritability and across many common sampling designs, type 1 error rates rise from a nominal 0.05 to 0.11. Conclusion Ignoring family structure may be useful in screening although it comes at a cost of a increased type 1 error rate, the magnitude of which depends on trait heritability and pedigree configuration. PMID:17570925

Genotyping-by-sequencing for Populus population genomics: An assessment of genome sampling patterns and filtering approaches

Treesearch

Martin P. Schilling; Paul G. Wolf; Aaron M. Duffy; Hardeep S. Rai; Carol A. Rowe; Bryce A. Richardson; Karen E. Mock

2014-01-01

Continuing advances in nucleotide sequencing technology are inspiring a suite of genomic approaches in studies of natural populations. Researchers are faced with data management and analytical scales that are increasing by orders of magnitude. With such dramatic advances comes a need to understand biases and error rates, which can be propagated and magnified in large-...

"Tools For Analysis and Visualization of Large Time- Varying CFD Data Sets"

NASA Technical Reports Server (NTRS)

Wilhelms, Jane; vanGelder, Allen

1999-01-01

During the four years of this grant (including the one year extension), we have explored many aspects of the visualization of large CFD (Computational Fluid Dynamics) datasets. These have included new direct volume rendering approaches, hierarchical methods, volume decimation, error metrics, parallelization, hardware texture mapping, and methods for analyzing and comparing images. First, we implemented an extremely general direct volume rendering approach that can be used to render rectilinear, curvilinear, or tetrahedral grids, including overlapping multiple zone grids, and time-varying grids. Next, we developed techniques for associating the sample data with a k-d tree, a simple hierarchial data model to approximate samples in the regions covered by each node of the tree, and an error metric for the accuracy of the model. We also explored a new method for determining the accuracy of approximate models based on the light field method described at ACM SIGGRAPH (Association for Computing Machinery Special Interest Group on Computer Graphics) '96. In our initial implementation, we automatically image the volume from 32 approximately evenly distributed positions on the surface of an enclosing tessellated sphere. We then calculate differences between these images under different conditions of volume approximation or decimation.

Numerical tilting compensation in microscopy based on wavefront sensing using transport of intensity equation method

NASA Astrophysics Data System (ADS)

Hu, Junbao; Meng, Xin; Wei, Qi; Kong, Yan; Jiang, Zhilong; Xue, Liang; Liu, Fei; Liu, Cheng; Wang, Shouyu

2018-03-01

Wide-field microscopy is commonly used for sample observations in biological research and medical diagnosis. However, the tilting error induced by the oblique location of the image recorder or the sample, as well as the inclination of the optical path often deteriorates the imaging quality. In order to eliminate the tilting in microscopy, a numerical tilting compensation technique based on wavefront sensing using transport of intensity equation method is proposed in this paper. Both the provided numerical simulations and practical experiments prove that the proposed technique not only accurately determines the tilting angle with simple setup and procedures, but also compensates the tilting error for imaging quality improvement even in the large tilting cases. Considering its simple systems and operations, as well as image quality improvement capability, it is believed the proposed method can be applied for tilting compensation in the optical microscopy.

Regression dilution in the proportional hazards model.

PubMed

Hughes, M D

1993-12-01

The problem of regression dilution arising from covariate measurement error is investigated for survival data using the proportional hazards model. The naive approach to parameter estimation is considered whereby observed covariate values are used, inappropriately, in the usual analysis instead of the underlying covariate values. A relationship between the estimated parameter in large samples and the true parameter is obtained showing that the bias does not depend on the form of the baseline hazard function when the errors are normally distributed. With high censorship, adjustment of the naive estimate by the factor 1 + lambda, where lambda is the ratio of within-person variability about an underlying mean level to the variability of these levels in the population sampled, removes the bias. As censorship increases, the adjustment required increases and when there is no censorship is markedly higher than 1 + lambda and depends also on the true risk relationship.

Measurement of breast-tissue x-ray attenuation by spectral mammography: solid lesions

NASA Astrophysics Data System (ADS)

Fredenberg, Erik; Kilburn-Toppin, Fleur; Willsher, Paula; Moa, Elin; Danielsson, Mats; Dance, David R.; Young, Kenneth C.; Wallis, Matthew G.

2016-04-01

Knowledge of x-ray attenuation is essential for developing and evaluating x-ray imaging technologies. For instance, techniques to distinguish between cysts and solid tumours at mammography screening would be highly desirable to reduce recalls, but the development requires knowledge of the x-ray attenuation for cysts and tumours. We have previously measured the attenuation of cyst fluid using photon-counting spectral mammography. Data on x-ray attenuation for solid breast lesions are available in the literature, but cover a relatively wide range, likely caused by natural spread between samples, random measurement errors, and different experimental conditions. In this study, we have adapted a previously developed spectral method to measure the linear attenuation of solid breast lesions. A total of 56 malignant and 5 benign lesions were included in the study. The samples were placed in a holder that allowed for thickness measurement. Spectral (energy-resolved) images of the samples were acquired and the image signal was mapped to equivalent thicknesses of two known reference materials, which can be used to derive the x-ray attenuation as a function of energy. The spread in equivalent material thicknesses was relatively large between samples, which is likely to be caused mainly by natural variation and only to a minor extent by random measurement errors and sample inhomogeneity. No significant difference in attenuation was found between benign and malignant solid lesions. The separation between cyst-fluid and tumour attenuation was, however, significant, which suggests it may be possible to distinguish cystic from solid breast lesions, and the results lay the groundwork for a clinical trial. In addition, the study adds a relatively large sample set to the published data and may contribute to a reduction in the overall uncertainty in the literature.

A new open-loop fiber optic gyro error compensation method based on angular velocity error modeling.

PubMed

Zhang, Yanshun; Guo, Yajing; Li, Chunyu; Wang, Yixin; Wang, Zhanqing

2015-02-27

With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage u and temperature T as the input variables and angular velocity error Δω as the output variable. Firstly, the angular velocity error Δω is extracted from OFOG output signals, and then the output voltage u, temperature T and angular velocity error Δω are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, u and Δω is established and thus Δω can be calculated automatically to compensate OFOG errors according to T and u. The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by 97.0%, 97.1% and 96.5% relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by 1.6%, 1.4% and 1.42%, respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity.

A New Open-Loop Fiber Optic Gyro Error Compensation Method Based on Angular Velocity Error Modeling

PubMed Central

Zhang, Yanshun; Guo, Yajing; Li, Chunyu; Wang, Yixin; Wang, Zhanqing

2015-01-01

With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage u and temperature T as the input variables and angular velocity error Δω as the output variable. Firstly, the angular velocity error Δω is extracted from OFOG output signals, and then the output voltage u, temperature T and angular velocity error Δω are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, u and Δω is established and thus Δω can be calculated automatically to compensate OFOG errors according to T and u. The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by 97.0%, 97.1% and 96.5% relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by 1.6%, 1.4% and 1.2%, respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity. PMID:25734642

78 FR 28597 - State Median Income Estimates for a Four-Person Household: Notice of the Federal Fiscal Year (FFY...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-15

....gov/acs/www/ or contact the Census Bureau's Social, Economic, and Housing Statistics Division at (301...) Sampling Error, which consists of the error that arises from the use of probability sampling to create the... direction; and (2) Sampling Error, which consists of the error that arises from the use of probability...

The Pearson-Readhead Survey of Compact Extragalactic Radio Sources from Space. I. The Images

NASA Astrophysics Data System (ADS)

Lister, M. L.; Tingay, S. J.; Murphy, D. W.; Piner, B. G.; Jones, D. L.; Preston, R. A.

2001-06-01

We present images from a space-VLBI survey using the facilities of the VLBI Space Observatory Programme (VSOP), drawing our sample from the well-studied Pearson-Readhead survey of extragalactic radio sources. Our survey has taken advantage of long space-VLBI baselines and large arrays of ground antennas, such as the Very Long Baseline Array and European VLBI Network, to obtain high-resolution images of 27 active galactic nuclei and to measure the core brightness temperatures of these sources more accurately than is possible from the ground. A detailed analysis of the source properties is given in accompanying papers. We have also performed an extensive series of simulations to investigate the errors in VSOP images caused by the relatively large holes in the (u,v)-plane when sources are observed near the orbit normal direction. We find that while the nominal dynamic range (defined as the ratio of map peak to off-source error) often exceeds 1000:1, the true dynamic range (map peak to on-source error) is only about 30:1 for relatively complex core-jet sources. For sources dominated by a strong point source, this value rises to approximately 100:1. We find the true dynamic range to be a relatively weak function of the difference in position angle (P.A.) between the jet P.A. and u-v coverage major axis P.A. For regions with low signal-to-noise ratios, typically located down the jet away from the core, large errors can occur, causing spurious features in VSOP images that should be interpreted with caution.

Transport Coefficients from Large Deviation Functions

NASA Astrophysics Data System (ADS)

Gao, Chloe; Limmer, David

2017-10-01

We describe a method for computing transport coefficients from the direct evaluation of large deviation function. This method is general, relying on only equilibrium fluctuations, and is statistically efficient, employing trajectory based importance sampling. Equilibrium fluctuations of molecular currents are characterized by their large deviation functions, which is a scaled cumulant generating function analogous to the free energy. A diffusion Monte Carlo algorithm is used to evaluate the large deviation functions, from which arbitrary transport coefficients are derivable. We find significant statistical improvement over traditional Green-Kubo based calculations. The systematic and statistical errors of this method are analyzed in the context of specific transport coefficient calculations, including the shear viscosity, interfacial friction coefficient, and thermal conductivity.

VizieR Online Data Catalog: Face-on disk galaxies photometry. I. (de Jong+, 1994)

NASA Astrophysics Data System (ADS)

de Jong, R. S.; van der Kruit, P. C.

1995-07-01

We present accurate surface photometry in the B, V, R, I, H and K passbands of 86 spiral galaxies. The galaxies in this statistically complete sample of undisturbed spirals were selected from the UGC to have minimum diameters of 2' and minor over major axis ratios larger than 0.625. This sample has been selected in such a way that it can be used to represent a volume limited sample. The observation and reduction techniques are described in detail, especially the not often used driftscan technique for CCDs and the relatively new techniques using near-infrared (near-IR) arrays. For each galaxy we present radial profiles of surface brightness. Using these profiles we calculated the integrated magnitudes of the galaxies in the different passbands. We performed internal and external consistency checks for the magnitudes as well as the luminosity profiles. The internal consistency is well within the estimated errors. Comparisons with other authors indicate that measurements from photographic plates can show large deviations in the zero-point magnitude. Our surface brightness profiles agree within the errors with other CCD measurements. The comparison of integrated magnitudes shows a large scatter, but a consistent zero-point. These measurements will be used in a series of forthcoming papers to discuss central surface brightnesses, scalelengths, colors and color gradients of disks of spiral galaxies. (9 data files).

Acceptance sampling for attributes via hypothesis testing and the hypergeometric distribution

NASA Astrophysics Data System (ADS)

Samohyl, Robert Wayne

2017-10-01

This paper questions some aspects of attribute acceptance sampling in light of the original concepts of hypothesis testing from Neyman and Pearson (NP). Attribute acceptance sampling in industry, as developed by Dodge and Romig (DR), generally follows the international standards of ISO 2859, and similarly the Brazilian standards NBR 5425 to NBR 5427 and the United States Standards ANSI/ASQC Z1.4. The paper evaluates and extends the area of acceptance sampling in two directions. First, by suggesting the use of the hypergeometric distribution to calculate the parameters of sampling plans avoiding the unnecessary use of approximations such as the binomial or Poisson distributions. We show that, under usual conditions, discrepancies can be large. The conclusion is that the hypergeometric distribution, ubiquitously available in commonly used software, is more appropriate than other distributions for acceptance sampling. Second, and more importantly, we elaborate the theory of acceptance sampling in terms of hypothesis testing rigorously following the original concepts of NP. By offering a common theoretical structure, hypothesis testing from NP can produce a better understanding of applications even beyond the usual areas of industry and commerce such as public health and political polling. With the new procedures, both sample size and sample error can be reduced. What is unclear in traditional acceptance sampling is the necessity of linking the acceptable quality limit (AQL) exclusively to the producer and the lot quality percent defective (LTPD) exclusively to the consumer. In reality, the consumer should also be preoccupied with a value of AQL, as should the producer with LTPD. Furthermore, we can also question why type I error is always uniquely associated with the producer as producer risk, and likewise, the same question arises with consumer risk which is necessarily associated with type II error. The resolution of these questions is new to the literature. The article presents R code throughout.

Internal pilots for a class of linear mixed models with Gaussian and compound symmetric data

PubMed Central

Gurka, Matthew J.; Coffey, Christopher S.; Muller, Keith E.

2015-01-01

SUMMARY An internal pilot design uses interim sample size analysis, without interim data analysis, to adjust the final number of observations. The approach helps to choose a sample size sufficiently large (to achieve the statistical power desired), but not too large (which would waste money and time). We report on recent research in cerebral vascular tortuosity (curvature in three dimensions) which would benefit greatly from internal pilots due to uncertainty in the parameters of the covariance matrix used for study planning. Unfortunately, observations correlated across the four regions of the brain and small sample sizes preclude using existing methods. However, as in a wide range of medical imaging studies, tortuosity data have no missing or mistimed data, a factorial within-subject design, the same between-subject design for all responses, and a Gaussian distribution with compound symmetry. For such restricted models, we extend exact, small sample univariate methods for internal pilots to linear mixed models with any between-subject design (not just two groups). Planning a new tortuosity study illustrates how the new methods help to avoid sample sizes that are too small or too large while still controlling the type I error rate. PMID:17318914

Blood transfusion sampling and a greater role for error recovery.

PubMed

Oldham, Jane

Patient identification errors in pre-transfusion blood sampling ('wrong blood in tube') are a persistent area of risk. These errors can potentially result in life-threatening complications. Current measures to address root causes of incidents and near misses have not resolved this problem and there is a need to look afresh at this issue. PROJECT PURPOSE: This narrative review of the literature is part of a wider system-improvement project designed to explore and seek a better understanding of the factors that contribute to transfusion sampling error as a prerequisite to examining current and potential approaches to error reduction. A broad search of the literature was undertaken to identify themes relating to this phenomenon. KEY DISCOVERIES: Two key themes emerged from the literature. Firstly, despite multi-faceted causes of error, the consistent element is the ever-present potential for human error. Secondly, current focus on error prevention could potentially be augmented with greater attention to error recovery. Exploring ways in which clinical staff taking samples might learn how to better identify their own errors is proposed to add to current safety initiatives.

Contextualizing individual differences in error monitoring: Links with impulsivity, negative affect, and conscientiousness.

PubMed

Hill, Kaylin E; Samuel, Douglas B; Foti, Dan

2016-08-01

The error-related negativity (ERN) is a neural measure of error processing that has been implicated as a neurobehavioral trait and has transdiagnostic links with psychopathology. Few studies, however, have contextualized this traitlike component with regard to dimensions of personality that, as intermediate constructs, may aid in contextualizing links with psychopathology. Accordingly, the aim of this study was to examine the interrelationships between error monitoring and dimensions of personality within a large adult sample (N = 208). Building on previous research, we found that the ERN relates to a combination of negative affect, impulsivity, and conscientiousness. At low levels of conscientiousness, negative urgency (i.e., impulsivity in the context of negative affect) predicted an increased ERN; at high levels of conscientiousness, the effect of negative urgency was not significant. This relationship was driven specifically by the conscientiousness facets of competence, order, and deliberation. Links between personality measures and error positivity amplitude were weaker and nonsignificant. Post-error slowing was also related to conscientiousness, as well as a different facet of impulsivity: lack of perseverance. These findings suggest that, in the general population, error processing is modulated by the joint combination of negative affect, impulsivity, and conscientiousness (i.e., the profile across traits), perhaps more so than any one dimension alone. This work may inform future research concerning aberrant error processing in clinical populations. © 2016 Society for Psychophysiological Research.

Validation of proton stopping power ratio estimation based on dual energy CT using fresh tissue samples

NASA Astrophysics Data System (ADS)

Taasti, Vicki T.; Michalak, Gregory J.; Hansen, David C.; Deisher, Amanda J.; Kruse, Jon J.; Krauss, Bernhard; Muren, Ludvig P.; Petersen, Jørgen B. B.; McCollough, Cynthia H.

2018-01-01

Dual energy CT (DECT) has been shown, in theoretical and phantom studies, to improve the stopping power ratio (SPR) determination used for proton treatment planning compared to the use of single energy CT (SECT). However, it has not been shown that this also extends to organic tissues. The purpose of this study was therefore to investigate the accuracy of SPR estimation for fresh pork and beef tissue samples used as surrogates of human tissues. The reference SPRs for fourteen tissue samples, which included fat, muscle and femur bone, were measured using proton pencil beams. The tissue samples were subsequently CT scanned using four different scanners with different dual energy acquisition modes, giving in total six DECT-based SPR estimations for each sample. The SPR was estimated using a proprietary algorithm (syngo.via DE Rho/Z Maps, Siemens Healthcare, Forchheim, Germany) for extracting the electron density and the effective atomic number. SECT images were also acquired and SECT-based SPR estimations were performed using a clinical Hounsfield look-up table. The mean and standard deviation of the SPR over large volume-of-interests were calculated. For the six different DECT acquisition methods, the root-mean-square errors (RMSEs) for the SPR estimates over all tissue samples were between 0.9% and 1.5%. For the SECT-based SPR estimation the RMSE was 2.8%. For one DECT acquisition method, a positive bias was seen in the SPR estimates, having a mean error of 1.3%. The largest errors were found in the very dense cortical bone from a beef femur. This study confirms the advantages of DECT-based SPR estimation although good results were also obtained using SECT for most tissues.

Using the Sampling Margin of Error to Assess the Interpretative Validity of Student Evaluations of Teaching

ERIC Educational Resources Information Center

James, David E.; Schraw, Gregory; Kuch, Fred

2015-01-01

We present an equation, derived from standard statistical theory, that can be used to estimate sampling margin of error for student evaluations of teaching (SETs). We use the equation to examine the effect of sample size, response rates and sample variability on the estimated sampling margin of error, and present results in four tables that allow…

SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification

PubMed Central

McClure, Matthew C.; McCarthy, John; Flynn, Paul; McClure, Jennifer C.; Dair, Emma; O'Connell, D. K.; Kearney, John F.

2018-01-01

A major use of genetic data is parentage verification and identification as inaccurate pedigrees negatively affect genetic gain. Since 2012 the international standard for single nucleotide polymorphism (SNP) verification in Bos taurus cattle has been the ISAG SNP panels. While these ISAG panels provide an increased level of parentage accuracy over microsatellite markers (MS), they can validate the wrong parent at ≤1% misconcordance rate levels, indicating that more SNP are needed if a more accurate pedigree is required. With rapidly increasing numbers of cattle being genotyped in Ireland that represent 61 B. taurus breeds from a wide range of farm types: beef/dairy, AI/pedigree/commercial, purebred/crossbred, and large to small herd size the Irish Cattle Breeding Federation (ICBF) analyzed different SNP densities to determine that at a minimum ≥500 SNP are needed to consistently predict only one set of parents at a ≤1% misconcordance rate. For parentage validation and prediction ICBF uses 800 SNP (ICBF800) selected based on SNP clustering quality, ISAG200 inclusion, call rate (CR), and minor allele frequency (MAF) in the Irish cattle population. Large datasets require sample and SNP quality control (QC). Most publications only deal with SNP QC via CR, MAF, parent-progeny conflicts, and Hardy-Weinberg deviation, but not sample QC. We report here parentage, SNP QC, and a genomic sample QC pipelines to deal with the unique challenges of >1 million genotypes from a national herd such as SNP genotype errors from mis-tagging of animals, lab errors, farm errors, and multiple other issues that can arise. We divide the pipeline into two parts: a Genotype QC and an Animal QC pipeline. The Genotype QC identifies samples with low call rate, missing or mixed genotype classes (no BB genotype or ABTG alleles present), and low genotype frequencies. The Animal QC handles situations where the genotype might not belong to the listed individual by identifying: >1 non-matching genotypes per animal, SNP duplicates, sex and breed prediction mismatches, parentage and progeny validation results, and other situations. The Animal QC pipeline make use of ICBF800 SNP set where appropriate to identify errors in a computationally efficient yet still highly accurate method. PMID:29599798

Essays in financial economics and econometrics

NASA Astrophysics Data System (ADS)

La Spada, Gabriele

Chapter 1 (my job market paper) asks the following question: Do asset managers reach for yield because of competitive pressures in a low rate environment? I propose a tournament model of money market funds (MMFs) to study this issue. I show that funds with different costs of default respond differently to changes in interest rates, and that it is important to distinguish the role of risk-free rates from that of risk premia. An increase in the risk premium leads funds with lower default costs to increase risk-taking, while funds with higher default costs reduce risk-taking. Without changes in the premium, low risk-free rates reduce risk-taking. My empirical analysis shows that these predictions are consistent with the risk-taking of MMFs during the 2006--2008 period. Chapter 2, co-authored with Fabrizio Lillo and published in Studies in Nonlinear Dynamics and Econometrics (2014), studies the effect of round-off error (or discretization) on stationary Gaussian long-memory process. For large lags, the autocovariance is rescaled by a factor smaller than one, and we compute this factor exactly. Hence, the discretized process has the same Hurst exponent as the underlying one. We show that in presence of round-off error, two common estimators of the Hurst exponent, the local Whittle (LW) estimator and the detrended fluctuation analysis (DFA), are severely negatively biased in finite samples. We derive conditions for consistency and asymptotic normality of the LW estimator applied to discretized processes and compute the asymptotic properties of the DFA for generic long-memory processes that encompass discretized processes. Chapter 3, co-authored with Fabrizio Lillo, studies the effect of round-off error on integrated Gaussian processes with possibly correlated increments. We derive the variance and kurtosis of the realized increment process in the limit of both "small" and "large" round-off errors, and its autocovariance for large lags. We propose novel estimators for the variance and lag-one autocorrelation of the underlying, unobserved increment process. We also show that for fractionally integrated processes, the realized increments have the same Hurst exponent as the underlying ones, but the LW estimator applied to the realized series is severely negatively biased in medium-sized samples.

Performance of bias-correction methods for exposure measurement error using repeated measurements with and without missing data.

PubMed

Batistatou, Evridiki; McNamee, Roseanne

2012-12-10

It is known that measurement error leads to bias in assessing exposure effects, which can however, be corrected if independent replicates are available. For expensive replicates, two-stage (2S) studies that produce data 'missing by design', may be preferred over a single-stage (1S) study, because in the second stage, measurement of replicates is restricted to a sample of first-stage subjects. Motivated by an occupational study on the acute effect of carbon black exposure on respiratory morbidity, we compare the performance of several bias-correction methods for both designs in a simulation study: an instrumental variable method (EVROS IV) based on grouping strategies, which had been recommended especially when measurement error is large, the regression calibration and the simulation extrapolation methods. For the 2S design, either the problem of 'missing' data was ignored or the 'missing' data were imputed using multiple imputations. Both in 1S and 2S designs, in the case of small or moderate measurement error, regression calibration was shown to be the preferred approach in terms of root mean square error. For 2S designs, regression calibration as implemented by Stata software is not recommended in contrast to our implementation of this method; the 'problematic' implementation of regression calibration although substantially improved with use of multiple imputations. The EVROS IV method, under a good/fairly good grouping, outperforms the regression calibration approach in both design scenarios when exposure mismeasurement is severe. Both in 1S and 2S designs with moderate or large measurement error, simulation extrapolation severely failed to correct for bias. Copyright © 2012 John Wiley & Sons, Ltd.

Large-Angular-Scale Clustering as a Clue to the Source of UHECRs

NASA Astrophysics Data System (ADS)

Berlind, Andreas A.; Farrar, Glennys R.

We explore what can be learned about the sources of UHECRs from their large-angular-scale clustering (referred to as their "bias" by the cosmology community). Exploiting the clustering on large scales has the advantage over small-scale correlations of being insensitive to uncertainties in source direction from magnetic smearing or measurement error. In a Cold Dark Matter cosmology, the amplitude of large-scale clustering depends on the mass of the system, with more massive systems such as galaxy clusters clustering more strongly than less massive systems such as ordinary galaxies or AGN. Therefore, studying the large-scale clustering of UHECRs can help determine a mass scale for their sources, given the assumption that their redshift depth is as expected from the GZK cutoff. We investigate the constraining power of a given UHECR sample as a function of its cutoff energy and number of events. We show that current and future samples should be able to distinguish between the cases of their sources being galaxy clusters, ordinary galaxies, or sources that are uncorrelated with the large-scale structure of the universe.

A large-scale test of free-energy simulation estimates of protein-ligand binding affinities.

PubMed

Mikulskis, Paulius; Genheden, Samuel; Ryde, Ulf

2014-10-27

We have performed a large-scale test of alchemical perturbation calculations with the Bennett acceptance-ratio (BAR) approach to estimate relative affinities for the binding of 107 ligands to 10 different proteins. Employing 20-Å truncated spherical systems and only one intermediate state in the perturbations, we obtain an error of less than 4 kJ/mol for 54% of the studied relative affinities and a precision of 0.5 kJ/mol on average. However, only four of the proteins gave acceptable errors, correlations, and rankings. The results could be improved by using nine intermediate states in the simulations or including the entire protein in the simulations using periodic boundary conditions. However, 27 of the calculated affinities still gave errors of more than 4 kJ/mol, and for three of the proteins the results were not satisfactory. This shows that the performance of BAR calculations depends on the target protein and that several transformations gave poor results owing to limitations in the molecular-mechanics force field or the restricted sampling possible within a reasonable simulation time. Still, the BAR results are better than docking calculations for most of the proteins.

Kullback-Leibler information function and the sequential selection of experiments to discriminate among several linear models

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

The error variance of the process prior multivariate normal distributions of the parameters of the models are assumed to be specified, prior probabilities of the models being correct. A rule for termination of sampling is proposed. Upon termination, the model with the largest posterior probability is chosen as correct. If sampling is not terminated, posterior probabilities of the models and posterior distributions of the parameters are computed. An experiment was chosen to maximize the expected Kullback-Leibler information function. Monte Carlo simulation experiments were performed to investigate large and small sample behavior of the sequential adaptive procedure.

Combining empirical approaches and error modelling to enhance predictive uncertainty estimation in extrapolation for operational flood forecasting. Tests on flood events on the Loire basin, France.

NASA Astrophysics Data System (ADS)

Berthet, Lionel; Marty, Renaud; Bourgin, François; Viatgé, Julie; Piotte, Olivier; Perrin, Charles

2017-04-01

An increasing number of operational flood forecasting centres assess the predictive uncertainty associated with their forecasts and communicate it to the end users. This information can match the end-users needs (i.e. prove to be useful for an efficient crisis management) only if it is reliable: reliability is therefore a key quality for operational flood forecasts. In 2015, the French flood forecasting national and regional services (Vigicrues network; www.vigicrues.gouv.fr) implemented a framework to compute quantitative discharge and water level forecasts and to assess the predictive uncertainty. Among the possible technical options to achieve this goal, a statistical analysis of past forecasting errors of deterministic models has been selected (QUOIQUE method, Bourgin, 2014). It is a data-based and non-parametric approach based on as few assumptions as possible about the forecasting error mathematical structure. In particular, a very simple assumption is made regarding the predictive uncertainty distributions for large events outside the range of the calibration data: the multiplicative error distribution is assumed to be constant, whatever the magnitude of the flood. Indeed, the predictive distributions may not be reliable in extrapolation. However, estimating the predictive uncertainty for these rare events is crucial when major floods are of concern. In order to improve the forecasts reliability for major floods, an attempt at combining the operational strength of the empirical statistical analysis and a simple error modelling is done. Since the heteroscedasticity of forecast errors can considerably weaken the predictive reliability for large floods, this error modelling is based on the log-sinh transformation which proved to reduce significantly the heteroscedasticity of the transformed error in a simulation context, even for flood peaks (Wang et al., 2012). Exploratory tests on some operational forecasts issued during the recent floods experienced in France (major spring floods in June 2016 on the Loire river tributaries and flash floods in fall 2016) will be shown and discussed. References Bourgin, F. (2014). How to assess the predictive uncertainty in hydrological modelling? An exploratory work on a large sample of watersheds, AgroParisTech Wang, Q. J., Shrestha, D. L., Robertson, D. E. and Pokhrel, P (2012). A log-sinh transformation for data normalization and variance stabilization. Water Resources Research, , W05514, doi:10.1029/2011WR010973

A priori evaluation of two-stage cluster sampling for accuracy assessment of large-area land-cover maps

USGS Publications Warehouse

Wickham, J.D.; Stehman, S.V.; Smith, J.H.; Wade, T.G.; Yang, L.

2004-01-01

Two-stage cluster sampling reduces the cost of collecting accuracy assessment reference data by constraining sample elements to fall within a limited number of geographic domains (clusters). However, because classification error is typically positively spatially correlated, within-cluster correlation may reduce the precision of the accuracy estimates. The detailed population information to quantify a priori the effect of within-cluster correlation on precision is typically unavailable. Consequently, a convenient, practical approach to evaluate the likely performance of a two-stage cluster sample is needed. We describe such an a priori evaluation protocol focusing on the spatial distribution of the sample by land-cover class across different cluster sizes and costs of different sampling options, including options not imposing clustering. This protocol also assesses the two-stage design's adequacy for estimating the precision of accuracy estimates for rare land-cover classes. We illustrate the approach using two large-area, regional accuracy assessments from the National Land-Cover Data (NLCD), and describe how the a priorievaluation was used as a decision-making tool when implementing the NLCD design.

Solid-Phase Extraction (SPE): Principles and Applications in Food Samples.

PubMed

Ötles, Semih; Kartal, Canan

2016-01-01

Solid-Phase Extraction (SPE) is a sample preparation method that is practised on numerous application fields due to its many advantages compared to other traditional methods. SPE was invented as an alternative to liquid/liquid extraction and eliminated multiple disadvantages, such as usage of large amount of solvent, extended operation time/procedure steps, potential sources of error, and high cost. Moreover, SPE can be plied to the samples combined with other analytical methods and sample preparation techniques optionally. SPE technique is a useful tool for many purposes through its versatility. Isolation, concentration, purification and clean-up are the main approaches in the practices of this method. Food structures represent a complicated matrix and can be formed into different physical stages, such as solid, viscous or liquid. Therefore, sample preparation step particularly has an important role for the determination of specific compounds in foods. SPE offers many opportunities not only for analysis of a large diversity of food samples but also for optimization and advances. This review aims to provide a comprehensive overview on basic principles of SPE and its applications for many analytes in food matrix.

Quantifying the Contributions of Environmental Parameters to Ceres Surface Net Radiation Error in China

NASA Astrophysics Data System (ADS)

Pan, X.; Yang, Y.; Liu, Y.; Fan, X.; Shan, L.; Zhang, X.

2018-04-01

Error source analyses are critical for the satellite-retrieved surface net radiation (Rn) products. In this study, we evaluate the Rn error sources in the Clouds and the Earth's Radiant Energy System (CERES) project at 43 sites from July in 2007 to December in 2007 in China. The results show that cloud fraction (CF), land surface temperature (LST), atmospheric temperature (AT) and algorithm error dominate the Rn error, with error contributions of -20, 15, 10 and 10 W/m2 (net shortwave (NSW)/longwave (NLW) radiation), respectively. For NSW, the dominant error source is algorithm error (more than 10 W/m2), particularly in spring and summer with abundant cloud. For NLW, due to the high sensitivity of algorithm and large LST/CF error, LST and CF are the largest error sources, especially in northern China. The AT influences the NLW error large in southern China because of the large AT error in there. The total precipitable water has weak influence on Rn error even with the high sensitivity of algorithm. In order to improve Rn quality, CF and LST (AT) error in northern (southern) China should be decreased.

Two-step single slope/SAR ADC with error correction for CMOS image sensor.

PubMed

Tang, Fang; Bermak, Amine; Amira, Abbes; Amor Benammar, Mohieddine; He, Debiao; Zhao, Xiaojin

2014-01-01

Conventional two-step ADC for CMOS image sensor requires full resolution noise performance in the first stage single slope ADC, leading to high power consumption and large chip area. This paper presents an 11-bit two-step single slope/successive approximation register (SAR) ADC scheme for CMOS image sensor applications. The first stage single slope ADC generates a 3-bit data and 1 redundant bit. The redundant bit is combined with the following 8-bit SAR ADC output code using a proposed error correction algorithm. Instead of requiring full resolution noise performance, the first stage single slope circuit of the proposed ADC can tolerate up to 3.125% quantization noise. With the proposed error correction mechanism, the power consumption and chip area of the single slope ADC are significantly reduced. The prototype ADC is fabricated using 0.18 μ m CMOS technology. The chip area of the proposed ADC is 7 μ m × 500 μ m. The measurement results show that the energy efficiency figure-of-merit (FOM) of the proposed ADC core is only 125 pJ/sample under 1.4 V power supply and the chip area efficiency is 84 k  μ m(2) · cycles/sample.

Statistical inference of seabed sound-speed structure in the Gulf of Oman Basin.

PubMed

Sagers, Jason D; Knobles, David P

2014-06-01

Addressed is the statistical inference of the sound-speed depth profile of a thick soft seabed from broadband sound propagation data recorded in the Gulf of Oman Basin in 1977. The acoustic data are in the form of time series signals recorded on a sparse vertical line array and generated by explosive sources deployed along a 280 km track. The acoustic data offer a unique opportunity to study a deep-water bottom-limited thickly sedimented environment because of the large number of time series measurements, very low seabed attenuation, and auxiliary measurements. A maximum entropy method is employed to obtain a conditional posterior probability distribution (PPD) for the sound-speed ratio and the near-surface sound-speed gradient. The multiple data samples allow for a determination of the average error constraint value required to uniquely specify the PPD for each data sample. Two complicating features of the statistical inference study are addressed: (1) the need to develop an error function that can both utilize the measured multipath arrival structure and mitigate the effects of data errors and (2) the effect of small bathymetric slopes on the structure of the bottom interacting arrivals.

Sample size determination in combinatorial chemistry.

PubMed Central

Zhao, P L; Zambias, R; Bolognese, J A; Boulton, D; Chapman, K

1995-01-01

Combinatorial chemistry is gaining wide appeal as a technique for generating molecular diversity. Among the many combinatorial protocols, the split/recombine method is quite popular and particularly efficient at generating large libraries of compounds. In this process, polymer beads are equally divided into a series of pools and each pool is treated with a unique fragment; then the beads are recombined, mixed to uniformity, and redivided equally into a new series of pools for the subsequent couplings. The deviation from the ideal equimolar distribution of the final products is assessed by a special overall relative error, which is shown to be related to the Pearson statistic. Although the split/recombine sampling scheme is quite different from those used in analysis of categorical data, the Pearson statistic is shown to still follow a chi2 distribution. This result allows us to derive the required number of beads such that, with 99% confidence, the overall relative error is controlled to be less than a pregiven tolerable limit L1. In this paper, we also discuss another criterion, which determines the required number of beads so that, with 99% confidence, all individual relative errors are controlled to be less than a pregiven tolerable limit L2 (0 < L2 < 1). PMID:11607586

Analysis of methods commonly used in biomedicine for treatment versus control comparison of very small samples.

PubMed

Ristić-Djurović, Jasna L; Ćirković, Saša; Mladenović, Pavle; Romčević, Nebojša; Trbovich, Alexander M

2018-04-01

A rough estimate indicated that use of samples of size not larger than ten is not uncommon in biomedical research and that many of such studies are limited to strong effects due to sample sizes smaller than six. For data collected from biomedical experiments it is also often unknown if mathematical requirements incorporated in the sample comparison methods are satisfied. Computer simulated experiments were used to examine performance of methods for qualitative sample comparison and its dependence on the effectiveness of exposure, effect intensity, distribution of studied parameter values in the population, and sample size. The Type I and Type II errors, their average, as well as the maximal errors were considered. The sample size 9 and the t-test method with p = 5% ensured error smaller than 5% even for weak effects. For sample sizes 6-8 the same method enabled detection of weak effects with errors smaller than 20%. If the sample sizes were 3-5, weak effects could not be detected with an acceptable error; however, the smallest maximal error in the most general case that includes weak effects is granted by the standard error of the mean method. The increase of sample size from 5 to 9 led to seven times more accurate detection of weak effects. Strong effects were detected regardless of the sample size and method used. The minimal recommended sample size for biomedical experiments is 9. Use of smaller sizes and the method of their comparison should be justified by the objective of the experiment. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparing the accuracy and precision of three techniques used for estimating missing landmarks when reconstructing fossil hominin crania.

PubMed

Neeser, Rudolph; Ackermann, Rebecca Rogers; Gain, James

2009-09-01

Various methodological approaches have been used for reconstructing fossil hominin remains in order to increase sample sizes and to better understand morphological variation. Among these, morphometric quantitative techniques for reconstruction are increasingly common. Here we compare the accuracy of three approaches--mean substitution, thin plate splines, and multiple linear regression--for estimating missing landmarks of damaged fossil specimens. Comparisons are made varying the number of missing landmarks, sample sizes, and the reference species of the population used to perform the estimation. The testing is performed on landmark data from individuals of Homo sapiens, Pan troglodytes and Gorilla gorilla, and nine hominin fossil specimens. Results suggest that when a small, same-species fossil reference sample is available to guide reconstructions, thin plate spline approaches perform best. However, if no such sample is available (or if the species of the damaged individual is uncertain), estimates of missing morphology based on a single individual (or even a small sample) of close taxonomic affinity are less accurate than those based on a large sample of individuals drawn from more distantly related extant populations using a technique (such as a regression method) able to leverage the information (e.g., variation/covariation patterning) contained in this large sample. Thin plate splines also show an unexpectedly large amount of error in estimating landmarks, especially over large areas. Recommendations are made for estimating missing landmarks under various scenarios. Copyright 2009 Wiley-Liss, Inc.

Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network

NASA Astrophysics Data System (ADS)

Wang, Zhen-yu; Zhang, Li-jie

2017-10-01

Measure error of the sensor can be effectively compensated with prediction. Aiming at large random drift error of MEMS(Micro Electro Mechanical System))gyroscope, an improved learning algorithm of Radial Basis Function(RBF) Neural Network(NN) based on K-means clustering and Orthogonal Least-Squares (OLS) is proposed in this paper. The algorithm selects the typical samples as the initial cluster centers of RBF NN firstly, candidates centers with K-means algorithm secondly, and optimizes the candidate centers with OLS algorithm thirdly, which makes the network structure simpler and makes the prediction performance better. Experimental results show that the proposed K-means clustering OLS learning algorithm can predict the random drift of MEMS gyroscope effectively, the prediction error of which is 9.8019e-007°/s and the prediction time of which is 2.4169e-006s

75 FR 26780 - State Median Income Estimate for a Four-Person Family: Notice of the Federal Fiscal Year (FFY...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-12

... Household Economic Statistics Division at (301) 763-3243. Under the advice of the Census Bureau, HHS..., which consists of the error that arises from the use of probability sampling to create the sample. For...) Sampling Error, which consists of the error that arises from the use of probability sampling to create the...

Verification of Satellite Rainfall Estimates from the Tropical Rainfall Measuring Mission over Ground Validation Sites

NASA Astrophysics Data System (ADS)

Fisher, B. L.; Wolff, D. B.; Silberstein, D. S.; Marks, D. M.; Pippitt, J. L.

2007-12-01

The Tropical Rainfall Measuring Mission's (TRMM) Ground Validation (GV) Program was originally established with the principal long-term goal of determining the random errors and systematic biases stemming from the application of the TRMM rainfall algorithms. The GV Program has been structured around two validation strategies: 1) determining the quantitative accuracy of the integrated monthly rainfall products at GV regional sites over large areas of about 500 km2 using integrated ground measurements and 2) evaluating the instantaneous satellite and GV rain rate statistics at spatio-temporal scales compatible with the satellite sensor resolution (Simpson et al. 1988, Thiele 1988). The GV Program has continued to evolve since the launch of the TRMM satellite on November 27, 1997. This presentation will discuss current GV methods of validating TRMM operational rain products in conjunction with ongoing research. The challenge facing TRMM GV has been how to best utilize rain information from the GV system to infer the random and systematic error characteristics of the satellite rain estimates. A fundamental problem of validating space-borne rain estimates is that the true mean areal rainfall is an ideal, scale-dependent parameter that cannot be directly measured. Empirical validation uses ground-based rain estimates to determine the error characteristics of the satellite-inferred rain estimates, but ground estimates also incur measurement errors and contribute to the error covariance. Furthermore, sampling errors, associated with the discrete, discontinuous temporal sampling by the rain sensors aboard the TRMM satellite, become statistically entangled in the monthly estimates. Sampling errors complicate the task of linking biases in the rain retrievals to the physics of the satellite algorithms. The TRMM Satellite Validation Office (TSVO) has made key progress towards effective satellite validation. For disentangling the sampling and retrieval errors, TSVO has developed and applied a methodology that statistically separates the two error sources. Using TRMM monthly estimates and high-resolution radar and gauge data, this method has been used to estimate sampling and retrieval error budgets over GV sites. More recently, a multi- year data set of instantaneous rain rates from the TRMM microwave imager (TMI), the precipitation radar (PR), and the combined algorithm was spatio-temporally matched and inter-compared to GV radar rain rates collected during satellite overpasses of select GV sites at the scale of the TMI footprint. The analysis provided a more direct probe of the satellite rain algorithms using ground data as an empirical reference. TSVO has also made significant advances in radar quality control through the development of the Relative Calibration Adjustment (RCA) technique. The RCA is currently being used to provide a long-term record of radar calibration for the radar at Kwajalein, a strategically important GV site in the tropical Pacific. The RCA technique has revealed previously undetected alterations in the radar sensitivity due to engineering changes (e.g., system modifications, antenna offsets, alterations of the receiver, or the data processor), making possible the correction of the radar rainfall measurements and ensuring the integrity of nearly a decade of TRMM GV observations and resources.

Efficient Computation of Anharmonic Force Constants via q-space, with Application to Graphene

NASA Astrophysics Data System (ADS)

Kornbluth, Mordechai; Marianetti, Chris

We present a new approach for extracting anharmonic force constants from a sparse sampling of the anharmonic dynamical tensor. We calculate the derivative of the energy with respect to q-space displacements (phonons) and strain, which guarantees the absence of supercell image errors. Central finite differences provide a well-converged quadratic error tail for each derivative, separating the contribution of each anharmonic order. These derivatives populate the anharmonic dynamical tensor in a sparse mesh that bounds the Brillouin Zone, which ensures comprehensive sampling of q-space while exploiting small-cell calculations for efficient, high-throughput computation. This produces a well-converged and precisely-defined dataset, suitable for big-data approaches. We transform this sparsely-sampled anharmonic dynamical tensor to real-space anharmonic force constants that obey full space-group symmetries by construction. Machine-learning techniques identify the range of real-space interactions. We show the entire process executed for graphene, up to and including the fifth-order anharmonic force constants. This method successfully calculates strain-based phonon renormalization in graphene, even under large strains, which solves a major shortcoming of previous potentials.

Estimating and comparing microbial diversity in the presence of sequencing errors

PubMed Central

Chiu, Chun-Huo

2016-01-01

Estimating and comparing microbial diversity are statistically challenging due to limited sampling and possible sequencing errors for low-frequency counts, producing spurious singletons. The inflated singleton count seriously affects statistical analysis and inferences about microbial diversity. Previous statistical approaches to tackle the sequencing errors generally require different parametric assumptions about the sampling model or about the functional form of frequency counts. Different parametric assumptions may lead to drastically different diversity estimates. We focus on nonparametric methods which are universally valid for all parametric assumptions and can be used to compare diversity across communities. We develop here a nonparametric estimator of the true singleton count to replace the spurious singleton count in all methods/approaches. Our estimator of the true singleton count is in terms of the frequency counts of doubletons, tripletons and quadrupletons, provided these three frequency counts are reliable. To quantify microbial alpha diversity for an individual community, we adopt the measure of Hill numbers (effective number of taxa) under a nonparametric framework. Hill numbers, parameterized by an order q that determines the measures’ emphasis on rare or common species, include taxa richness (q = 0), Shannon diversity (q = 1, the exponential of Shannon entropy), and Simpson diversity (q = 2, the inverse of Simpson index). A diversity profile which depicts the Hill number as a function of order q conveys all information contained in a taxa abundance distribution. Based on the estimated singleton count and the original non-singleton frequency counts, two statistical approaches (non-asymptotic and asymptotic) are developed to compare microbial diversity for multiple communities. (1) A non-asymptotic approach refers to the comparison of estimated diversities of standardized samples with a common finite sample size or sample completeness. This approach aims to compare diversity estimates for equally-large or equally-complete samples; it is based on the seamless rarefaction and extrapolation sampling curves of Hill numbers, specifically for q = 0, 1 and 2. (2) An asymptotic approach refers to the comparison of the estimated asymptotic diversity profiles. That is, this approach compares the estimated profiles for complete samples or samples whose size tends to be sufficiently large. It is based on statistical estimation of the true Hill number of any order q ≥ 0. In the two approaches, replacing the spurious singleton count by our estimated count, we can greatly remove the positive biases associated with diversity estimates due to spurious singletons and also make fair comparisons across microbial communities, as illustrated in our simulation results and in applying our method to analyze sequencing data from viral metagenomes. PMID:26855872

Errors in radial velocity variance from Doppler wind lidar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, H.; Barthelmie, R. J.; Doubrawa, P.

A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Our paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration, using both statistically simulated and observed data. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, themore » systematic error is negligible but the random error exceeds about 10%.« less

Errors in radial velocity variance from Doppler wind lidar

DOE PAGES

Wang, H.; Barthelmie, R. J.; Doubrawa, P.; ...

2016-08-29

A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Our paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration, using both statistically simulated and observed data. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, themore » systematic error is negligible but the random error exceeds about 10%.« less

Calibration approach for extremely variable laser induced plasmas and a strategy to reduce the matrix effect in general

NASA Astrophysics Data System (ADS)

Lazic, V.; De Ninno, A.

2017-11-01

The laser induced plasma spectroscopy was applied on particles attached on substrate represented by a silica wafer covered with a thin oil film. The substrate itself weakly interacts with a ns Nd:YAG laser (1064 nm) while presence of particles strongly enhances the plasma emission, here detected by a compact spectrometer array. Variations of the sample mass from one laser spot to another exceed one order of magnitude, as estimated by on-line photography and the initial image calibration for different sample loadings. Consequently, the spectral lines from particles show extreme intensity fluctuations from one sampling point to another, between the detection threshold and the detector's saturation in some cases. In such conditions the common calibration approach based on the averaged spectra, also when considering ratios of the element lines i.e. concentrations, produces errors too large for measuring the sample compositions. On the other hand, intensities of an analytical and the reference line from single shot spectra are linearly correlated. The corresponding slope depends on the concentration ratio and it is weakly sensitive to fluctuations of the plasma temperature inside the data set. A use of the slopes for constructing the calibration graphs significantly reduces the error bars but it does not eliminate the point scattering caused by the matrix effect, which is also responsible for large differences in the average plasma temperatures among the samples. Well aligned calibration points were obtained after identifying the couples of transitions less sensitive to variations of the plasma temperature, and this was achieved by simple theoretical simulations. Such selection of the analytical lines minimizes the matrix effect, and together with the chosen calibration approach, allows to measure the relative element concentrations even in highly unstable laser induced plasmas.

CERAMIC: Case-Control Association Testing in Samples with Related Individuals, Based on Retrospective Mixed Model Analysis with Adjustment for Covariates

PubMed Central

Zhong, Sheng; McPeek, Mary Sara

2016-01-01

We consider the problem of genetic association testing of a binary trait in a sample that contains related individuals, where we adjust for relevant covariates and allow for missing data. We propose CERAMIC, an estimating equation approach that can be viewed as a hybrid of logistic regression and linear mixed-effects model (LMM) approaches. CERAMIC extends the recently proposed CARAT method to allow samples with related individuals and to incorporate partially missing data. In simulations, we show that CERAMIC outperforms existing LMM and generalized LMM approaches, maintaining high power and correct type 1 error across a wider range of scenarios. CERAMIC results in a particularly large power increase over existing methods when the sample includes related individuals with some missing data (e.g., when some individuals with phenotype and covariate information have missing genotype), because CERAMIC is able to make use of the relationship information to incorporate partially missing data in the analysis while correcting for dependence. Because CERAMIC is based on a retrospective analysis, it is robust to misspecification of the phenotype model, resulting in better control of type 1 error and higher power than that of prospective methods, such as GMMAT, when the phenotype model is misspecified. CERAMIC is computationally efficient for genomewide analysis in samples of related individuals of almost any configuration, including small families, unrelated individuals and even large, complex pedigrees. We apply CERAMIC to data on type 2 diabetes (T2D) from the Framingham Heart Study. In a genome scan, 9 of the 10 smallest CERAMIC p-values occur in or near either known T2D susceptibility loci or plausible candidates, verifying that CERAMIC is able to home in on the important loci in a genome scan. PMID:27695091

Volumetric error modeling, identification and compensation based on screw theory for a large multi-axis propeller-measuring machine

NASA Astrophysics Data System (ADS)

Zhong, Xuemin; Liu, Hongqi; Mao, Xinyong; Li, Bin; He, Songping; Peng, Fangyu

2018-05-01

Large multi-axis propeller-measuring machines have two types of geometric error, position-independent geometric errors (PIGEs) and position-dependent geometric errors (PDGEs), which both have significant effects on the volumetric error of the measuring tool relative to the worktable. This paper focuses on modeling, identifying and compensating for the volumetric error of the measuring machine. A volumetric error model in the base coordinate system is established based on screw theory considering all the geometric errors. In order to fully identify all the geometric error parameters, a new method for systematic measurement and identification is proposed. All the PIGEs of adjacent axes and the six PDGEs of the linear axes are identified with a laser tracker using the proposed model. Finally, a volumetric error compensation strategy is presented and an inverse kinematic solution for compensation is proposed. The final measuring and compensation experiments have further verified the efficiency and effectiveness of the measuring and identification method, indicating that the method can be used in volumetric error compensation for large machine tools.

Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Data Set

NASA Technical Reports Server (NTRS)

Lau, William K. M. (Technical Monitor); Bell, Thomas L.; Steiner, Matthias; Zhang, Yu; Wood, Eric F.

2002-01-01

The uncertainty of rainfall estimated from averages of discrete samples collected by a satellite is assessed using a multi-year radar data set covering a large portion of the United States. The sampling-related uncertainty of rainfall estimates is evaluated for all combinations of 100 km, 200 km, and 500 km space domains, 1 day, 5 day, and 30 day rainfall accumulations, and regular sampling time intervals of 1 h, 3 h, 6 h, 8 h, and 12 h. These extensive analyses are combined to characterize the sampling uncertainty as a function of space and time domain, sampling frequency, and rainfall characteristics by means of a simple scaling law. Moreover, it is shown that both parametric and non-parametric statistical techniques of estimating the sampling uncertainty produce comparable results. Sampling uncertainty estimates, however, do depend on the choice of technique for obtaining them. They can also vary considerably from case to case, reflecting the great variability of natural rainfall, and should therefore be expressed in probabilistic terms. Rainfall calibration errors are shown to affect comparison of results obtained by studies based on data from different climate regions and/or observation platforms.

Fundamental Bounds for Sequence Reconstruction from Nanopore Sequencers.

PubMed

Magner, Abram; Duda, Jarosław; Szpankowski, Wojciech; Grama, Ananth

2016-06-01

Nanopore sequencers are emerging as promising new platforms for high-throughput sequencing. As with other technologies, sequencer errors pose a major challenge for their effective use. In this paper, we present a novel information theoretic analysis of the impact of insertion-deletion (indel) errors in nanopore sequencers. In particular, we consider the following problems: (i) for given indel error characteristics and rate, what is the probability of accurate reconstruction as a function of sequence length; (ii) using replicated extrusion (the process of passing a DNA strand through the nanopore), what is the number of replicas needed to accurately reconstruct the true sequence with high probability? Our results provide a number of important insights: (i) the probability of accurate reconstruction of a sequence from a single sample in the presence of indel errors tends quickly (i.e., exponentially) to zero as the length of the sequence increases; and (ii) replicated extrusion is an effective technique for accurate reconstruction. We show that for typical distributions of indel errors, the required number of replicas is a slow function (polylogarithmic) of sequence length - implying that through replicated extrusion, we can sequence large reads using nanopore sequencers. Moreover, we show that in certain cases, the required number of replicas can be related to information-theoretic parameters of the indel error distributions.

Distribution-Preserving Stratified Sampling for Learning Problems.

PubMed

Cervellera, Cristiano; Maccio, Danilo

2017-06-09

The need for extracting a small sample from a large amount of real data, possibly streaming, arises routinely in learning problems, e.g., for storage, to cope with computational limitations, obtain good training/test/validation sets, and select minibatches for stochastic gradient neural network training. Unless we have reasons to select the samples in an active way dictated by the specific task and/or model at hand, it is important that the distribution of the selected points is as similar as possible to the original data. This is obvious for unsupervised learning problems, where the goal is to gain insights on the distribution of the data, but it is also relevant for supervised problems, where the theory explains how the training set distribution influences the generalization error. In this paper, we analyze the technique of stratified sampling from the point of view of distances between probabilities. This allows us to introduce an algorithm, based on recursive binary partition of the input space, aimed at obtaining samples that are distributed as much as possible as the original data. A theoretical analysis is proposed, proving the (greedy) optimality of the procedure together with explicit error bounds. An adaptive version of the algorithm is also introduced to cope with streaming data. Simulation tests on various data sets and different learning tasks are also provided.

[Comparison study on sampling methods of Oncomelania hupensis snail survey in marshland schistosomiasis epidemic areas in China].

PubMed

An, Zhao; Wen-Xin, Zhang; Zhong, Yao; Yu-Kuan, Ma; Qing, Liu; Hou-Lang, Duan; Yi-di, Shang

2016-06-29

To optimize and simplify the survey method of Oncomelania hupensis snail in marshland endemic region of schistosomiasis and increase the precision, efficiency and economy of the snail survey. A quadrate experimental field was selected as the subject of 50 m×50 m size in Chayegang marshland near Henghu farm in the Poyang Lake region and a whole-covered method was adopted to survey the snails. The simple random sampling, systematic sampling and stratified random sampling methods were applied to calculate the minimum sample size, relative sampling error and absolute sampling error. The minimum sample sizes of the simple random sampling, systematic sampling and stratified random sampling methods were 300, 300 and 225, respectively. The relative sampling errors of three methods were all less than 15%. The absolute sampling errors were 0.221 7, 0.302 4 and 0.047 8, respectively. The spatial stratified sampling with altitude as the stratum variable is an efficient approach of lower cost and higher precision for the snail survey.

Translating Climate Projections for Bridge Engineering

NASA Astrophysics Data System (ADS)

Anderson, C.; Takle, E. S.; Krajewski, W.; Mantilla, R.; Quintero, F.

2015-12-01

A bridge vulnerability pilot study was conducted by Iowa Department of Transportation (IADOT) as one of nineteen pilots supported by the Federal Highway Administration Climate Change Resilience Pilots. Our pilot study team consisted of the IADOT senior bridge engineer who is the preliminary design section leader as well as climate and hydrological scientists. The pilot project culminated in a visual graphic designed by the bridge engineer (Figure 1), and an evaluation framework for bridge engineering design. The framework has four stages. The first two stages evaluate the spatial and temporal resolution needed in climate projection data in order to be suitable for input to a hydrology model. The framework separates streamflow simulation error into errors from the streamflow model and from the coarseness of input weather data series. In the final two stages, the framework evaluates credibility of climate projection streamflow simulations. Using an empirically downscaled data set, projection streamflow is generated. Error is computed in two time frames: the training period of the empirical downscaling methodology, and an out-of-sample period. If large errors in projection streamflow were observed during the training period, it would indicate low accuracy and, therefore, low credibility. If large errors in streamflow were observed during the out-of-sample period, it would mean the approach may not include some causes of change and, therefore, the climate projections would have limited credibility for setting expectations for changes. We address uncertainty with confidence intervals on quantiles of streamflow discharge. The results show the 95% confidence intervals have significant overlap. Nevertheless, the use of confidence intervals enabled engineering judgement. In our discussions, we noted the consistency in direction of change across basins, though the flood mechanism was different across basins, and the high bound of bridge lifetime period quantiles exceeded that of the historical period. This suggested the change was not isolated, and it systemically altered the risk profile. One suggestion to incorporate engineering judgement was to consider degrees of vulnerability using the median discharge of the historical period and the upper bound discharge for the bridge lifetime period.

Clinical biochemistry laboratory rejection rates due to various types of preanalytical errors.

PubMed

Atay, Aysenur; Demir, Leyla; Cuhadar, Serap; Saglam, Gulcan; Unal, Hulya; Aksun, Saliha; Arslan, Banu; Ozkan, Asuman; Sutcu, Recep

2014-01-01

Preanalytical errors, along the process from the beginning of test requests to the admissions of the specimens to the laboratory, cause the rejection of samples. The aim of this study was to better explain the reasons of rejected samples, regarding to their rates in certain test groups in our laboratory. This preliminary study was designed on the rejected samples in one-year period, based on the rates and types of inappropriateness. Test requests and blood samples of clinical chemistry, immunoassay, hematology, glycated hemoglobin, coagulation and erythrocyte sedimentation rate test units were evaluated. Types of inappropriateness were evaluated as follows: improperly labelled samples, hemolysed, clotted specimen, insufficient volume of specimen and total request errors. A total of 5,183,582 test requests from 1,035,743 blood collection tubes were considered. The total rejection rate was 0.65 %. The rejection rate of coagulation group was significantly higher (2.28%) than the other test groups (P < 0.001) including insufficient volume of specimen error rate as 1.38%. Rejection rates of hemolysis, clotted specimen and insufficient volume of sample error were found to be 8%, 24% and 34%, respectively. Total request errors, particularly, for unintelligible requests were 32% of the total for inpatients. The errors were especially attributable to unintelligible requests of inappropriate test requests, improperly labelled samples for inpatients and blood drawing errors especially due to insufficient volume of specimens in a coagulation test group. Further studies should be performed after corrective and preventive actions to detect a possible decrease in rejecting samples.

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2018-04-01

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance - Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The `best' performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample prediction via partial least squares - discriminant analysis (PLS2-DA). Error rate of each model was estimated repeatedly via: (a) AP on full data (APF, error); (b) AP on training set (APS, error); and (c) ET on the respective test set (ETS, error). A good PLS2-DA model is expected to produce APS, error and EVS, error that is similar to the APF, error. Bearing that in mind, the similarities between (a) APS, error vs. APF, error; (b) ETS, error vs. APF, error and; (c) APS, error vs. ETS, error were evaluated using correlation tests (i.e. Pearson and Spearman's rank test), using series of PLS2-DA models computed from KS-set and IRS-set, respectively. Overall, models constructed from IRS-set exhibits more similarities between the internal and external error rates than the respective KS-set, i.e. less risk of overfitting. In conclusion, IRS is more reliable than KS in sampling representative training set.

Improvements in medical quality and patient safety through implementation of a case bundle management strategy in a large outpatient blood collection center.

PubMed

Zhao, Shuzhen; He, Lujia; Feng, Chenchen; He, Xiaoli

2018-06-01

Laboratory errors in blood collection center (BCC) are most common in the preanalytical phase. It is, therefore, of vital importance for administrators to take measures to improve healthcare quality and patient safety.In 2015, a case bundle management strategy was applied in a large outpatient BCC to improve its medical quality and patient safety.Unqualified blood sampling, complications, patient waiting time, largest number of patients waiting during peak hours, patient complaints, and patient satisfaction were compared over the period from 2014 to 2016.The strategy reduced unqualified blood sampling, complications, patient waiting time, largest number of patients waiting during peak hours, and patient complaints, while improving patient satisfaction.This strategy was effective in improving BCC healthcare quality and patient safety.

Generalized Variance Function Applications in Forestry

Treesearch

James Alegria; Charles T. Scott; Charles T. Scott

1991-01-01

Adequately predicting the sampling errors of tabular data can reduce printing costs by eliminating the need to publish separate sampling error tables. Two generalized variance functions (GVFs) found in the literature and three GVFs derived for this study were evaluated for their ability to predict the sampling error of tabular forestry estimates. The recommended GVFs...

Controlling the type I error rate in two-stage sequential adaptive designs when testing for average bioequivalence.

PubMed

Maurer, Willi; Jones, Byron; Chen, Ying

2018-05-10

In a 2×2 crossover trial for establishing average bioequivalence (ABE) of a generic agent and a currently marketed drug, the recommended approach to hypothesis testing is the two one-sided test (TOST) procedure, which depends, among other things, on the estimated within-subject variability. The power of this procedure, and therefore the sample size required to achieve a minimum power, depends on having a good estimate of this variability. When there is uncertainty, it is advisable to plan the design in two stages, with an interim sample size reestimation after the first stage, using an interim estimate of the within-subject variability. One method and 3 variations of doing this were proposed by Potvin et al. Using simulation, the operating characteristics, including the empirical type I error rate, of the 4 variations (called Methods A, B, C, and D) were assessed by Potvin et al and Methods B and C were recommended. However, none of these 4 variations formally controls the type I error rate of falsely claiming ABE, even though the amount of inflation produced by Method C was considered acceptable. A major disadvantage of assessing type I error rate inflation using simulation is that unless all possible scenarios for the intended design and analysis are investigated, it is impossible to be sure that the type I error rate is controlled. Here, we propose an alternative, principled method of sample size reestimation that is guaranteed to control the type I error rate at any given significance level. This method uses a new version of the inverse-normal combination of p-values test, in conjunction with standard group sequential techniques, that is more robust to large deviations in initial assumptions regarding the variability of the pharmacokinetic endpoints. The sample size reestimation step is based on significance levels and power requirements that are conditional on the first-stage results. This necessitates a discussion and exploitation of the peculiar properties of the power curve of the TOST testing procedure. We illustrate our approach with an example based on a real ABE study and compare the operating characteristics of our proposed method with those of Method B of Povin et al. Copyright © 2018 John Wiley & Sons, Ltd.

Irregular analytical errors in diagnostic testing - a novel concept.

PubMed

Vogeser, Michael; Seger, Christoph

2018-02-23

In laboratory medicine, routine periodic analyses for internal and external quality control measurements interpreted by statistical methods are mandatory for batch clearance. Data analysis of these process-oriented measurements allows for insight into random analytical variation and systematic calibration bias over time. However, in such a setting, any individual sample is not under individual quality control. The quality control measurements act only at the batch level. Quantitative or qualitative data derived for many effects and interferences associated with an individual diagnostic sample can compromise any analyte. It is obvious that a process for a quality-control-sample-based approach of quality assurance is not sensitive to such errors. To address the potential causes and nature of such analytical interference in individual samples more systematically, we suggest the introduction of a new term called the irregular (individual) analytical error. Practically, this term can be applied in any analytical assay that is traceable to a reference measurement system. For an individual sample an irregular analytical error is defined as an inaccuracy (which is the deviation from a reference measurement procedure result) of a test result that is so high it cannot be explained by measurement uncertainty of the utilized routine assay operating within the accepted limitations of the associated process quality control measurements. The deviation can be defined as the linear combination of the process measurement uncertainty and the method bias for the reference measurement system. Such errors should be coined irregular analytical errors of the individual sample. The measurement result is compromised either by an irregular effect associated with the individual composition (matrix) of the sample or an individual single sample associated processing error in the analytical process. Currently, the availability of reference measurement procedures is still highly limited, but LC-isotope-dilution mass spectrometry methods are increasingly used for pre-market validation of routine diagnostic assays (these tests also involve substantial sets of clinical validation samples). Based on this definition/terminology, we list recognized causes of irregular analytical error as a risk catalog for clinical chemistry in this article. These issues include reproducible individual analytical errors (e.g. caused by anti-reagent antibodies) and non-reproducible, sporadic errors (e.g. errors due to incorrect pipetting volume due to air bubbles in a sample), which can both lead to inaccurate results and risks for patients.

Interval sampling methods and measurement error: a computer simulation.

PubMed

Wirth, Oliver; Slaven, James; Taylor, Matthew A

2014-01-01

A simulation study was conducted to provide a more thorough account of measurement error associated with interval sampling methods. A computer program simulated the application of momentary time sampling, partial-interval recording, and whole-interval recording methods on target events randomly distributed across an observation period. The simulation yielded measures of error for multiple combinations of observation period, interval duration, event duration, and cumulative event duration. The simulations were conducted up to 100 times to yield measures of error variability. Although the present simulation confirmed some previously reported characteristics of interval sampling methods, it also revealed many new findings that pertain to each method's inherent strengths and weaknesses. The analysis and resulting error tables can help guide the selection of the most appropriate sampling method for observation-based behavioral assessments. © Society for the Experimental Analysis of Behavior.

Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2014-06-01

The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.

Quantizing and sampling considerations in digital phased-locked loops

NASA Technical Reports Server (NTRS)

Hurst, G. T.; Gupta, S. C.

1974-01-01

The quantizer problem is first considered. The conditions under which the uniform white sequence model for the quantizer error is valid are established independent of the sampling rate. An equivalent spectral density is defined for the quantizer error resulting in an effective SNR value. This effective SNR may be used to determine quantized performance from infinitely fine quantized results. Attention is given to sampling rate considerations. Sampling rate characteristics of the digital phase-locked loop (DPLL) structure are investigated for the infinitely fine quantized system. The predicted phase error variance equation is examined as a function of the sampling rate. Simulation results are presented and a method is described which enables the minimum required sampling rate to be determined from the predicted phase error variance equations.

Minimizing Artifacts and Biases in Chamber-Based Measurements of Soil Respiration

NASA Astrophysics Data System (ADS)

Davidson, E. A.; Savage, K.

2001-05-01

Soil respiration is one of the largest and most important fluxes of carbon in terrestrial ecosystems. The objectives of this paper are to review concerns about uncertainties of chamber-based measurements of CO2 emissions from soils, to evaluate the direction and magnitude of these potential errors, and to explain procedures that minimize these errors and biases. Disturbance of diffusion gradients cause underestimate of fluxes by less than 15% in most cases, and can be partially corrected for with curve fitting and/or can be minimized by using brief measurement periods. Under-pressurization or over-pressurization of the chamber caused by flow restrictions in air circulating designs can cause large errors, but can also be avoided with properly sized chamber vents and unrestricted flows. Somewhat larger pressure differentials are observed under windy conditions, and the accuracy of measurements made under such conditions needs more research. Spatial and temporal heterogeneity can be addressed with appropriate chamber sizes and numbers and frequency of sampling. For example, means of 8 randomly chosen flux measurements from a population of 36 measurements made with 300 cm2 chambers in tropical forests and pastures were within 25% of the full population mean 98% of the time and were within 10% of the full population mean 70% of the time. Comparisons of chamber-based measurements with tower-based measurements of total ecosystem respiration require analysis of the scale of variation within the purported tower footprint. In a forest at Howland, Maine, the differences in soil respiration rates among very poorly drained and well drained soils were large, but they mostly were fortuitously cancelled when evaluated for purported tower footprints of 600-2100 m length. While all of these potential sources of measurement error and sampling biases must be carefully considered, properly designed and deployed chambers provide a reliable means of accurately measuring soil respiration in terrestrial ecosystems.

How large are the consequences of covariate imbalance in cluster randomized trials: a simulation study with a continuous outcome and a binary covariate at the cluster level.

PubMed

Moerbeek, Mirjam; van Schie, Sander

2016-07-11

The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform.

PubMed

Schirmer, Melanie; Ijaz, Umer Z; D'Amore, Rosalinda; Hall, Neil; Sloan, William T; Quince, Christopher

2015-03-31

With read lengths of currently up to 2 × 300 bp, high throughput and low sequencing costs Illumina's MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Discriminant WSRC for Large-Scale Plant Species Recognition.

PubMed

Zhang, Shanwen; Zhang, Chuanlei; Zhu, Yihai; You, Zhuhong

2017-01-01

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.

A robust clustering algorithm for identifying problematic samples in genome-wide association studies.

PubMed

Bellenguez, Céline; Strange, Amy; Freeman, Colin; Donnelly, Peter; Spencer, Chris C A

2012-01-01

High-throughput genotyping arrays provide an efficient way to survey single nucleotide polymorphisms (SNPs) across the genome in large numbers of individuals. Downstream analysis of the data, for example in genome-wide association studies (GWAS), often involves statistical models of genotype frequencies across individuals. The complexities of the sample collection process and the potential for errors in the experimental assay can lead to biases and artefacts in an individual's inferred genotypes. Rather than attempting to model these complications, it has become a standard practice to remove individuals whose genome-wide data differ from the sample at large. Here we describe a simple, but robust, statistical algorithm to identify samples with atypical summaries of genome-wide variation. Its use as a semi-automated quality control tool is demonstrated using several summary statistics, selected to identify different potential problems, and it is applied to two different genotyping platforms and sample collections. The algorithm is written in R and is freely available at www.well.ox.ac.uk/chris-spencer chris.spencer@well.ox.ac.uk Supplementary data are available at Bioinformatics online.

Discrepancies in reporting the CAG repeat lengths for Huntington's disease

PubMed Central

Quarrell, Oliver W; Handley, Olivia; O'Donovan, Kirsty; Dumoulin, Christine; Ramos-Arroyo, Maria; Biunno, Ida; Bauer, Peter; Kline, Margaret; Landwehrmeyer, G Bernhard

2012-01-01

Huntington's disease results from a CAG repeat expansion within the Huntingtin gene; this is measured routinely in diagnostic laboratories. The European Huntington's Disease Network REGISTRY project centrally measures CAG repeat lengths on fresh samples; these were compared with the original results from 121 laboratories across 15 countries. We report on 1326 duplicate results; a discrepancy in reporting the upper allele occurred in 51% of cases, this reduced to 13.3% and 9.7% when we applied acceptable measurement errors proposed by the American College of Medical Genetics and the Draft European Best Practice Guidelines, respectively. Duplicate results were available for 1250 lower alleles; discrepancies occurred in 40% of cases. Clinically significant discrepancies occurred in 4.0% of cases with a potential unexplained misdiagnosis rate of 0.3%. There was considerable variation in the discrepancy rate among 10 of the countries participating in this study. Out of 1326 samples, 348 were re-analysed by an accredited diagnostic laboratory, based in Germany, with concordance rates of 93% and 94% for the upper and lower alleles, respectively. This became 100% if the acceptable measurement errors were applied. The central laboratory correctly reported allele sizes for six standard reference samples, blind to the known result. Our study differs from external quality assessment (EQA) schemes in that these are duplicate results obtained from a large sample of patients across the whole diagnostic range. We strongly recommend that laboratories state an error rate for their measurement on the report, participate in EQA schemes and use reference materials regularly to adjust their own internal standards. PMID:21811303

Development of a Work Control System for Propulsion Testing at NASA Stennis

NASA Technical Reports Server (NTRS)

Messer, Elizabeth A.

2005-01-01

This paper will explain the requirements and steps taken to develop the current Propulsion Test Directorate electronic work control system for Test Operations. The PTD Work Control System includes work authorization and technical instruction documents, such as test preparation sheets, discrepancy reports, test requests, pre-test briefing reports, and other test operations supporting tools. The environment that existed in the E-Complex test areas in the late 1990's was one of enormous growth which brought people of diverse backgrounds together for the sole purpose of testing propulsion hardware. The problem that faced us was that these newly formed teams did not have a consistent and clearly understood method for writing, performing or verifying work. A paper system was developed that would allow the teams to use the same forms, but this still presented problems in the large amount of errors occurring, such as lost paperwork and inconsistent implementation. In a sampling of errors in August 1999, the paper work control system encountered 250 errors out of 230 documents released and completed, for an error rate of 111%.

Robust estimation of adaptive tensors of curvature by tensor voting.

PubMed

Tong, Wai-Shun; Tang, Chi-Keung

2005-03-01

Although curvature estimation from a given mesh or regularly sampled point set is a well-studied problem, it is still challenging when the input consists of a cloud of unstructured points corrupted by misalignment error and outlier noise. Such input is ubiquitous in computer vision. In this paper, we propose a three-pass tensor voting algorithm to robustly estimate curvature tensors, from which accurate principal curvatures and directions can be calculated. Our quantitative estimation is an improvement over the previous two-pass algorithm, where only qualitative curvature estimation (sign of Gaussian curvature) is performed. To overcome misalignment errors, our improved method automatically corrects input point locations at subvoxel precision, which also rejects outliers that are uncorrectable. To adapt to different scales locally, we define the RadiusHit of a curvature tensor to quantify estimation accuracy and applicability. Our curvature estimation algorithm has been proven with detailed quantitative experiments, performing better in a variety of standard error metrics (percentage error in curvature magnitudes, absolute angle difference in curvature direction) in the presence of a large amount of misalignment noise.

Adjusting for multiple prognostic factors in the analysis of randomised trials

PubMed Central

2013-01-01

Background When multiple prognostic factors are adjusted for in the analysis of a randomised trial, it is unclear (1) whether it is necessary to account for each of the strata, formed by all combinations of the prognostic factors (stratified analysis), when randomisation has been balanced within each stratum (stratified randomisation), or whether adjusting for the main effects alone will suffice, and (2) the best method of adjustment in terms of type I error rate and power, irrespective of the randomisation method. Methods We used simulation to (1) determine if a stratified analysis is necessary after stratified randomisation, and (2) to compare different methods of adjustment in terms of power and type I error rate. We considered the following methods of analysis: adjusting for covariates in a regression model, adjusting for each stratum using either fixed or random effects, and Mantel-Haenszel or a stratified Cox model depending on outcome. Results Stratified analysis is required after stratified randomisation to maintain correct type I error rates when (a) there are strong interactions between prognostic factors, and (b) there are approximately equal number of patients in each stratum. However, simulations based on real trial data found that type I error rates were unaffected by the method of analysis (stratified vs unstratified), indicating these conditions were not met in real datasets. Comparison of different analysis methods found that with small sample sizes and a binary or time-to-event outcome, most analysis methods lead to either inflated type I error rates or a reduction in power; the lone exception was a stratified analysis using random effects for strata, which gave nominal type I error rates and adequate power. Conclusions It is unlikely that a stratified analysis is necessary after stratified randomisation except in extreme scenarios. Therefore, the method of analysis (accounting for the strata, or adjusting only for the covariates) will not generally need to depend on the method of randomisation used. Most methods of analysis work well with large sample sizes, however treating strata as random effects should be the analysis method of choice with binary or time-to-event outcomes and a small sample size. PMID:23898993

Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint.

PubMed

Saito, Priscila T M; Nakamura, Rodrigo Y M; Amorim, Willian P; Papa, João P; de Rezende, Pedro J; Falcão, Alexandre X

2015-01-01

Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presents a methodology to compare classifiers with respect to their ability to learn from classification errors on a large learning set, within a given time limit. Faster techniques may acquire more training samples, but only when they are more effective will they achieve higher performance on unseen testing sets. We demonstrate this result using several techniques, multiple datasets, and typical learning-time limits required by applications.

Quantification of Greenhouse Gas Emission Rates from strong Point Sources by Space-borne IPDA Lidar Measurements: Results from a Sensitivity Analysis Study

NASA Astrophysics Data System (ADS)

Ehret, G.; Kiemle, C.; Rapp, M.

2017-12-01

The practical implementation of the Paris Agreement (COP21) vastly profit from an independent, reliable and global measurement system of greenhouse gas emissions, in particular of CO2, in order to complement and cross-check national efforts. Most fossil-fuel CO2 emitters emanate from large sources such as cities and power plants. These emissions increase the local CO2 abundance in the atmosphere by 1-10 parts per million (ppm) which is a signal that is significantly larger than the variability from natural sources and sinks over the local source domain. Despite these large signals, they are only sparsely sampled by the ground-based network which calls for satellite measurements. However, none of the existing and forthcoming passive satellite instruments, operating in the NIR spectral domain, can measure CO2 emissions at night time or in low sunlight conditions and in high latitude regions in winter times. The resulting sparse coverage of passive spectrometers is a serious limitation, particularly for the Northern Hemisphere, since these regions exhibit substantial emissions during the winter as well as other times of the year. In contrast, CO2 measurements by an Integrated Path Differential Absorption (IPDA) Lidar are largely immune to these limitations and initial results from airborne application look promising. In this study, we discuss the implication for a space-borne IPDA Lidar system. A Gaussian plume model will be used to simulate the CO2-distribution of large power plants downstream to the source. The space-borne measurements are simulated by applying a simple forward model based on Gaussian error distribution. Besides the sampling frequency, the sampling geometry (e.g. measurement distance to the emitting source) and the error of the measurement itself vastly impact on the flux inversion performance. We will discuss the results by incorporating Gaussian plume and mass budget approaches to quantify the emission rates.

Ensemble codes involving hippocampal neurons are at risk during delayed performance tests.

PubMed

Hampson, R E; Deadwyler, S A

1996-11-26

Multielectrode recording techniques were used to record ensemble activity from 10 to 16 simultaneously active CA1 and CA3 neurons in the rat hippocampus during performance of a spatial delayed-nonmatch-to-sample task. Extracted sources of variance were used to assess the nature of two different types of errors that accounted for 30% of total trials. The two types of errors included ensemble "miscodes" of sample phase information and errors associated with delay-dependent corruption or disappearance of sample information at the time of the nonmatch response. Statistical assessment of trial sequences and associated "strength" of hippocampal ensemble codes revealed that miscoded error trials always followed delay-dependent error trials in which encoding was "weak," indicating that the two types of errors were "linked." It was determined that the occurrence of weakly encoded, delay-dependent error trials initiated an ensemble encoding "strategy" that increased the chances of being correct on the next trial and avoided the occurrence of further delay-dependent errors. Unexpectedly, the strategy involved "strongly" encoding response position information from the prior (delay-dependent) error trial and carrying it forward to the sample phase of the next trial. This produced a miscode type error on trials in which the "carried over" information obliterated encoding of the sample phase response on the next trial. Application of this strategy, irrespective of outcome, was sufficient to reorient the animal to the proper between trial sequence of response contingencies (nonmatch-to-sample) and boost performance to 73% correct on subsequent trials. The capacity for ensemble analyses of strength of information encoding combined with statistical assessment of trial sequences therefore provided unique insight into the "dynamic" nature of the role hippocampus plays in delay type memory tasks.

A new statistic to express the uncertainty of kriging predictions for purposes of survey planning.

NASA Astrophysics Data System (ADS)

Lark, R. M.; Lapworth, D. J.

2014-05-01

It is well-known that one advantage of kriging for spatial prediction is that, given the random effects model, the prediction error variance can be computed a priori for alternative sampling designs. This allows one to compare sampling schemes, in particular sampling at different densities, and so to decide on one which meets requirements in terms of the uncertainty of the resulting predictions. However, the planning of sampling schemes must account not only for statistical considerations, but also logistics and cost. This requires effective communication between statisticians, soil scientists and data users/sponsors such as managers, regulators or civil servants. In our experience the latter parties are not necessarily able to interpret the prediction error variance as a measure of uncertainty for decision making. In some contexts (particularly the solution of very specific problems at large cartographic scales, e.g. site remediation and precision farming) it is possible to translate uncertainty of predictions into a loss function directly comparable with the cost incurred in increasing precision. Often, however, sampling must be planned for more generic purposes (e.g. baseline or exploratory geochemical surveys). In this latter context the prediction error variance may be of limited value to a non-statistician who has to make a decision on sample intensity and associated cost. We propose an alternative criterion for these circumstances to aid communication between statisticians and data users about the uncertainty of geostatistical surveys based on different sampling intensities. The criterion is the consistency of estimates made from two non-coincident instantiations of a proposed sample design. We consider square sample grids, one instantiation is offset from the second by half the grid spacing along the rows and along the columns. If a sample grid is coarse relative to the important scales of variation in the target property then the consistency of predictions from two instantiations is expected to be small, and can be increased by reducing the grid spacing. The measure of consistency is the correlation between estimates from the two instantiations of the sample grid, averaged over a grid cell. We call this the offset correlation, it can be calculated from the variogram. We propose that this measure is easier to grasp intuitively than the prediction error variance, and has the advantage of having an upper bound (1.0) which will aid its interpretation. This quality measure is illustrated for some hypothetical examples, considering both ordinary kriging and factorial kriging of the variable of interest. It is also illustrated using data on metal concentrations in the soil of north-east England.

Using snowball sampling method with nurses to understand medication administration errors.

PubMed

Sheu, Shuh-Jen; Wei, Ien-Lan; Chen, Ching-Huey; Yu, Shu; Tang, Fu-In

2009-02-01

We aimed to encourage nurses to release information about drug administration errors to increase understanding of error-related circumstances and to identify high-alert situations. Drug administration errors represent the majority of medication errors, but errors are underreported. Effective ways are lacking to encourage nurses to actively report errors. Snowball sampling was conducted to recruit participants. A semi-structured questionnaire was used to record types of error, hospital and nurse backgrounds, patient consequences, error discovery mechanisms and reporting rates. Eighty-five nurses participated, reporting 328 administration errors (259 actual, 69 near misses). Most errors occurred in medical surgical wards of teaching hospitals, during day shifts, committed by nurses working fewer than two years. Leading errors were wrong drugs and doses, each accounting for about one-third of total errors. Among 259 actual errors, 83.8% resulted in no adverse effects; among remaining 16.2%, 6.6% had mild consequences and 9.6% had serious consequences (severe reaction, coma, death). Actual errors and near misses were discovered mainly through double-check procedures by colleagues and nurses responsible for errors; reporting rates were 62.5% (162/259) vs. 50.7% (35/69) and only 3.5% (9/259) vs. 0% (0/69) were disclosed to patients and families. High-alert situations included administration of 15% KCl, insulin and Pitocin; using intravenous pumps; and implementation of cardiopulmonary resuscitation (CPR). Snowball sampling proved to be an effective way to encourage nurses to release details concerning medication errors. Using empirical data, we identified high-alert situations. Strategies for reducing drug administration errors by nurses are suggested. Survey results suggest that nurses should double check medication administration in known high-alert situations. Nursing management can use snowball sampling to gather error details from nurses in a non-reprimanding atmosphere, helping to establish standard operational procedures for known high-alert situations.

Design and analysis of a sub-aperture scanning machine for the transmittance measurements of large-aperture optical system

NASA Astrophysics Data System (ADS)

He, Yingwei; Li, Ping; Feng, Guojin; Cheng, Li; Wang, Yu; Wu, Houping; Liu, Zilong; Zheng, Chundi; Sha, Dingguo

2010-11-01

For measuring large-aperture optical system transmittance, a novel sub-aperture scanning machine with double-rotating arms (SSMDA) was designed to obtain sub-aperture beam spot. Optical system full-aperture transmittance measurements can be achieved by applying sub-aperture beam spot scanning technology. The mathematical model of the SSMDA based on a homogeneous coordinate transformation matrix is established to develop a detailed methodology for analyzing the beam spot scanning errors. The error analysis methodology considers two fundamental sources of scanning errors, namely (1) the length systematic errors and (2) the rotational systematic errors. As the systematic errors of the parameters are given beforehand, computational results of scanning errors are between -0.007~0.028mm while scanning radius is not lager than 400.000mm. The results offer theoretical and data basis to the research on transmission characteristics of large optical system.

Population size estimation in Yellowstone wolves with error-prone noninvasive microsatellite genotypes.

PubMed

Creel, Scott; Spong, Goran; Sands, Jennifer L; Rotella, Jay; Zeigle, Janet; Joe, Lawrence; Murphy, Kerry M; Smith, Douglas

2003-07-01

Determining population sizes can be difficult, but is essential for conservation. By counting distinct microsatellite genotypes, DNA from noninvasive samples (hair, faeces) allows estimation of population size. Problems arise because genotypes from noninvasive samples are error-prone, but genotyping errors can be reduced by multiple polymerase chain reaction (PCR). For faecal genotypes from wolves in Yellowstone National Park, error rates varied substantially among samples, often above the 'worst-case threshold' suggested by simulation. Consequently, a substantial proportion of multilocus genotypes held one or more errors, despite multiple PCR. These genotyping errors created several genotypes per individual and caused overestimation (up to 5.5-fold) of population size. We propose a 'matching approach' to eliminate this overestimation bias.

Ten years of preanalytical monitoring and control: Synthetic Balanced Score Card Indicator

PubMed Central

López-Garrigós, Maite; Flores, Emilio; Santo-Quiles, Ana; Gutierrez, Mercedes; Lugo, Javier; Lillo, Rosa; Leiva-Salinas, Carlos

2015-01-01

Introduction Preanalytical control and monitoring continue to be an important issue for clinical laboratory professionals. The aim of the study was to evaluate a monitoring system of preanalytical errors regarding not suitable samples for analysis, based on different indicators; to compare such indicators in different phlebotomy centres; and finally to evaluate a single synthetic preanalytical indicator that may be included in the balanced scorecard management system (BSC). Materials and methods We collected individual and global preanalytical errors in haematology, coagulation, chemistry, and urine samples analysis. We also analyzed a synthetic indicator that represents the sum of all types of preanalytical errors, expressed in a sigma level. We studied the evolution of those indicators over time and compared indicator results by way of the comparison of proportions and Chi-square. Results There was a decrease in the number of errors along the years (P < 0.001). This pattern was confirmed in primary care patients, inpatients and outpatients. In blood samples, fewer errors occurred in outpatients, followed by inpatients. Conclusion We present a practical and effective methodology to monitor unsuitable sample preanalytical errors. The synthetic indicator results summarize overall preanalytical sample errors, and can be used as part of BSC management system. PMID:25672466

Inspection error and its adverse effects - A model with implications for practitioners

NASA Technical Reports Server (NTRS)

Collins, R. D., Jr.; Case, K. E.; Bennett, G. K.

1978-01-01

Inspection error has clearly been shown to have adverse effects upon the results desired from a quality assurance sampling plan. These effects upon performance measures have been well documented from a statistical point of view. However, little work has been presented to convince the QC manager of the unfavorable cost consequences resulting from inspection error. This paper develops a very general, yet easily used, mathematical cost model. The basic format of the well-known Guthrie-Johns model is used. However, it is modified as required to assess the effects of attributes sampling errors of the first and second kind. The economic results, under different yet realistic conditions, will no doubt be of interest to QC practitioners who face similar problems daily. Sampling inspection plans are optimized to minimize economic losses due to inspection error. Unfortunately, any error at all results in some economic loss which cannot be compensated for by sampling plan design; however, improvements over plans which neglect the presence of inspection error are possible. Implications for human performance betterment programs are apparent, as are trade-offs between sampling plan modification and inspection and training improvements economics.

Quantization of high dimensional Gaussian vector using permutation modulation with application to information reconciliation in continuous variable QKD

NASA Astrophysics Data System (ADS)

Daneshgaran, Fred; Mondin, Marina; Olia, Khashayar

This paper is focused on the problem of Information Reconciliation (IR) for continuous variable Quantum Key Distribution (QKD). The main problem is quantization and assignment of labels to the samples of the Gaussian variables observed at Alice and Bob. Trouble is that most of the samples, assuming that the Gaussian variable is zero mean which is de-facto the case, tend to have small magnitudes and are easily disturbed by noise. Transmission over longer and longer distances increases the losses corresponding to a lower effective Signal-to-Noise Ratio (SNR) exasperating the problem. Quantization over higher dimensions is advantageous since it allows for fractional bit per sample accuracy which may be needed at very low SNR conditions whereby the achievable secret key rate is significantly less than one bit per sample. In this paper, we propose to use Permutation Modulation (PM) for quantization of Gaussian vectors potentially containing thousands of samples. PM is applied to the magnitudes of the Gaussian samples and we explore the dependence of the sign error probability on the magnitude of the samples. At very low SNR, we may transmit the entire label of the PM code from Bob to Alice in Reverse Reconciliation (RR) over public channel. The side information extracted from this label can then be used by Alice to characterize the sign error probability of her individual samples. Forward Error Correction (FEC) coding can be used by Bob on each subset of samples with similar sign error probability to aid Alice in error correction. This can be done for different subsets of samples with similar sign error probabilities leading to an Unequal Error Protection (UEP) coding paradigm.

Measurement equivalence of seven selected items of posttraumatic growth between black and white adult survivors of Hurricane Katrina.

PubMed

Rhodes, Alison M; Tran, Thanh V

2013-02-01

This study examined the equivalence or comparability of the measurement properties of seven selected items measuring posttraumatic growth among self-identified Black (n = 270) and White (n = 707) adult survivors of Hurricane Katrina, using data from the Baseline Survey of the Hurricane Katrina Community Advisory Group Study. Internal consistency reliability was equally good for both groups (Cronbach's alphas = .79), as were correlations between individual scale items and their respective overall scale. Confirmatory factor analysis of a congeneric measurement model of seven selected items of posttraumatic growth showed adequate measures of fit for both groups. The results showed only small variation in magnitude of factor loadings and measurement errors between the two samples. Tests of measurement invariance showed mixed results, but overall indicated that factor loading, error variance, and factor variance were similar between the two samples. These seven selected items can be useful for future large-scale surveys of posttraumatic growth.

A novel visual pipework inspection system

NASA Astrophysics Data System (ADS)

Summan, Rahul; Jackson, William; Dobie, Gordon; MacLeod, Charles; Mineo, Carmelo; West, Graeme; Offin, Douglas; Bolton, Gary; Marshall, Stephen; Lille, Alexandre

2018-04-01

The interior visual inspection of pipelines in the nuclear industry is a safety critical activity conducted during outages to ensure the continued safe and reliable operation of plant. Typically, the video output by a manually deployed probe is viewed by an operator looking to identify and localize surface defects such as corrosion, erosion and pitting. However, it is very challenging to estimate the nature and extent of defects by viewing a large structure through a relatively small field of view. This work describes a new visual inspection system employing photogrammetry using a fisheye camera and a structured light system to map the internal geometry of pipelines by generating a photorealistic, geometrically accurate surface model. The error of the system output was evaluated through comparison to a ground truth laser scan (ATOS GOM Triple Scan) of a nuclear grade split pipe sample (stainless steel 304L, 80mm internal diameter) containing defects representative of the application - the error was found to be submillimeter across the sample.

Array coding for large data memories

NASA Technical Reports Server (NTRS)

Tranter, W. H.

1982-01-01

It is pointed out that an array code is a convenient method for storing large quantities of data. In a typical application, the array consists of N data words having M symbols in each word. The probability of undetected error is considered, taking into account three symbol error probabilities which are of interest, and a formula for determining the probability of undetected error. Attention is given to the possibility of reading data into the array using a digital communication system with symbol error probability p. Two different schemes are found to be of interest. The conducted analysis of array coding shows that the probability of undetected error is very small even for relatively large arrays.

Numerical modeling of the divided bar measurements

NASA Astrophysics Data System (ADS)

LEE, Y.; Keehm, Y.

2011-12-01

The divided-bar technique has been used to measure thermal conductivity of rocks and fragments in heat flow studies. Though widely used, divided-bar measurements can have errors, which are not systematically quantified yet. We used an FEM and performed a series of numerical studies to evaluate various errors in divided-bar measurements and to suggest more reliable measurement techniques. A divided-bar measurement should be corrected against lateral heat loss on the sides of rock samples, and the thermal resistance at the contacts between the rock sample and the bar. We first investigated how the amount of these corrections would change by the thickness and thermal conductivity of rock samples through numerical modeling. When we fixed the sample thickness as 10 mm and varied thermal conductivity, errors in the measured thermal conductivity ranges from 2.02% for 1.0 W/m/K to 7.95% for 4.0 W/m/K. While we fixed thermal conductivity as 1.38 W/m/K and varied the sample thickness, we found that the error ranges from 2.03% for the 30 mm-thick sample to 11.43% for the 5 mm-thick sample. After corrections, a variety of error analyses for divided-bar measurements were conducted numerically. Thermal conductivity of two thin standard disks (2 mm in thickness) located at the top and the bottom of the rock sample slightly affects the accuracy of thermal conductivity measurements. When the thermal conductivity of a sample is 3.0 W/m/K and that of two standard disks is 0.2 W/m/K, the relative error in measured thermal conductivity is very small (~0.01%). However, the relative error would reach up to -2.29% for the same sample when thermal conductivity of two disks is 0.5 W/m/K. The accuracy of thermal conductivity measurements strongly depends on thermal conductivity and the thickness of thermal compound that is applied to reduce thermal resistance at contacts between the rock sample and the bar. When the thickness of thermal compound (0.29 W/m/K) is 0.03 mm, we found that the relative error in measured thermal conductivity is 4.01%, while the relative error can be very significant (~12.2%) if the thickness increases to 0.1 mm. Then, we fixed the thickness (0.03 mm) and varied thermal conductivity of the thermal compound. We found that the relative error with an 1.0 W/m/K compound is 1.28%, and the relative error with a 0.29 W/m/K is 4.06%. When we repeated this test with a different thickness of the thermal compound (0.1 mm), the relative error with an 1.0 W/m/K compound is 3.93%, and that with a 0.29 W/m/K is 12.2%. In addition, the cell technique by Sass et al.(1971), which is widely used to measure thermal conductivity of rock fragments, was evaluated using the FEM modeling. A total of 483 isotropic and homogeneous spherical rock fragments in the sample holder were used to test numerically the accuracy of the cell technique. The result shows the relative error of -9.61% for rock fragments with the thermal conductivity of 2.5 W/m/K. In conclusion, we report quantified errors in the divided-bar and the cell technique for thermal conductivity measurements for rocks and fragments. We found that the FEM modeling can accurately mimic these measurement techniques and can help us to estimate measurement errors quantitatively.

How accurate are lexile text measures?

PubMed

Stenner, A Jackson; Burdick, Hal; Sanford, Eleanor E; Burdick, Donald S

2006-01-01

The Lexile Framework for Reading models comprehension as the difference between a reader measure and a text measure. Uncertainty in comprehension rates results from unreliability in reader measures and inaccuracy in text readability measures. Whole-text processing eliminates sampling error in text measures. However, Lexile text measures are imperfect due to misspecification of the Lexile theory. The standard deviation component associated with theory misspecification is estimated at 64L for a standard-length passage (approximately 125 words). A consequence is that standard errors for longer texts (2,500 to 150,000 words) are measured on the Lexile scale with uncertainties in the single digits. Uncertainties in expected comprehension rates are largely due to imprecision in reader ability and not inaccuracies in text readabilities.

The effects of cracks on the quantification of the cancellous bone fabric tensor in fossil and archaeological specimens: a simulation study.

PubMed

Bishop, Peter J; Clemente, Christofer J; Hocknull, Scott A; Barrett, Rod S; Lloyd, David G

2017-03-01

Cancellous bone is very sensitive to its prevailing mechanical environment, and study of its architecture has previously aided interpretations of locomotor biomechanics in extinct animals or archaeological populations. However, quantification of architectural features may be compromised by poor preservation in fossil and archaeological specimens, such as post mortem cracking or fracturing. In this study, the effects of post mortem cracks on the quantification of cancellous bone fabric were investigated through the simulation of cracks in otherwise undamaged modern bone samples. The effect on both scalar (degree of fabric anisotropy, fabric elongation index) and vector (principal fabric directions) variables was assessed through comparing the results of architectural analyses of cracked vs. non-cracked samples. Error was found to decrease as the relative size of the crack decreased, and as the orientation of the crack approached the orientation of the primary fabric direction. However, even in the best-case scenario simulated, error remained substantial, with at least 18% of simulations showing a > 10% error when scalar variables were considered, and at least 6.7% of simulations showing a > 10° error when vector variables were considered. As a 10% (scalar) or 10° (vector) difference is probably too large for reliable interpretation of a fossil or archaeological specimen, these results suggest that cracks should be avoided if possible when analysing cancellous bone architecture in such specimens. © 2016 Anatomical Society.

Simulation techniques for estimating error in the classification of normal patterns

NASA Technical Reports Server (NTRS)

Whitsitt, S. J.; Landgrebe, D. A.

1974-01-01

Methods of efficiently generating and classifying samples with specified multivariate normal distributions were discussed. Conservative confidence tables for sample sizes are given for selective sampling. Simulation results are compared with classified training data. Techniques for comparing error and separability measure for two normal patterns are investigated and used to display the relationship between the error and the Chernoff bound.

Large tree diameter distribution modelling using sparse airborne laser scanning data in a subtropical forest in Nepal

NASA Astrophysics Data System (ADS)

Rana, Parvez; Vauhkonen, Jari; Junttila, Virpi; Hou, Zhengyang; Gautam, Basanta; Cawkwell, Fiona; Tokola, Timo

2017-12-01

Large-diameter trees (taking DBH > 30 cm to define large trees) dominate the dynamics, function and structure of a forest ecosystem. The aim here was to employ sparse airborne laser scanning (ALS) data with a mean point density of 0.8 m-2 and the non-parametric k-most similar neighbour (k-MSN) to predict tree diameter at breast height (DBH) distributions in a subtropical forest in southern Nepal. The specific objectives were: (1) to evaluate the accuracy of the large-tree fraction of the diameter distribution; and (2) to assess the effect of the number of training areas (sample size, n) on the accuracy of the predicted tree diameter distribution. Comparison of the predicted distributions with empirical ones indicated that the large tree diameter distribution can be derived in a mixed species forest with a RMSE% of 66% and a bias% of -1.33%. It was also feasible to downsize the sample size without losing the interpretability capacity of the model. For large-diameter trees, even a reduction of half of the training plots (n = 250), giving a marginal increase in the RMSE% (1.12-1.97%) was reported compared with the original training plots (n = 500). To be consistent with these outcomes, the sample areas should capture the entire range of spatial and feature variability in order to reduce the occurrence of error.

A method to estimate the effect of deformable image registration uncertainties on daily dose mapping

PubMed Central

Murphy, Martin J.; Salguero, Francisco J.; Siebers, Jeffrey V.; Staub, David; Vaman, Constantin

2012-01-01

Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties. PMID:22320766

Optimal weighting in fNL constraints from large scale structure in an idealised case

NASA Astrophysics Data System (ADS)

Slosar, Anže

2009-03-01

We consider the problem of optimal weighting of tracers of structure for the purpose of constraining the non-Gaussianity parameter fNL. We work within the Fisher matrix formalism expanded around fiducial model with fNL = 0 and make several simplifying assumptions. By slicing a general sample into infinitely many samples with different biases, we derive the analytic expression for the relevant Fisher matrix element. We next consider weighting schemes that construct two effective samples from a single sample of tracers with a continuously varying bias. We show that a particularly simple ansatz for weighting functions can recover all information about fNL in the initial sample that is recoverable using a given bias observable and that simple division into two equal samples is considerably suboptimal when sampling of modes is good, but only marginally suboptimal in the limit where Poisson errors dominate.

A correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation.

PubMed

Sokolenko, Stanislav; Aucoin, Marc G

2015-09-04

The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct systematic error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed. Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and systematic errors. To detect systematic errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify systematic errors as small as 2.5 % under a wide range of conditions. Both the simulation framework and error correction method represent examples of time-course analysis that can be applied to further developments in (1)H-NMR methodology and the more general application of quantitative metabolomics.

Traditional Nurse Triage vs. Physician Tele-Presence in a Pediatric Emergency Department

PubMed Central

Marconi, Greg P.; Chang, Todd; Pham, Phung K.; Grajower, Daniel N.; Nager, Alan L.

2014-01-01

Objectives To compare traditional nurse triage (TNT) in a Pediatric Emergency Department (PED) to physician tele-presence (PTP). Methods Prospective, 2×2 crossover study with random assignment using a sample of walk-in patients seeking care in a PED at a large, tertiary care children’s hospital, from May 2012 to January 2013. Outcomes of triage times, documentation errors, triage scores, and survey responses were compared between TNT and PTP. Comparison between PTP to actual treating PED physicians regarding the accuracy of ordering blood and urine tests, throat cultures, and radiologic imaging was also studied. Results Paired samples t-tests showed a statistically significant difference in triage time between TNT and PTP (p=0.03), but no significant difference in documentation errors (p=0.10). Triage scores of TNT were 71% accurate, compared to PTP, which were 95% accurate. Both parents and children had favorable scores regarding PTP and the majority indicated they would prefer PTP again at their next PED visit. PTP diagnostic ordering was comparable to the actual PED physician ordering, showing no statistical differences. Conclusions Utilizing physician tele-presence technology to remotely perform triage is a feasible alternative to traditional nurse triage, with no clinically significant differences in time, triage scores, errors and patient and parent satisfaction. PMID:24445223

Interpolation for de-Dopplerisation

NASA Astrophysics Data System (ADS)

Graham, W. R.

2018-05-01

'De-Dopplerisation' is one aspect of a problem frequently encountered in experimental acoustics: deducing an emitted source signal from received data. It is necessary when source and receiver are in relative motion, and requires interpolation of the measured signal. This introduces error. In acoustics, typical current practice is to employ linear interpolation and reduce error by over-sampling. In other applications, more advanced approaches with better performance have been developed. Associated with this work is a large body of theoretical analysis, much of which is highly specialised. Nonetheless, a simple and compact performance metric is available: the Fourier transform of the 'kernel' function underlying the interpolation method. Furthermore, in the acoustics context, it is a more appropriate indicator than other, more abstract, candidates. On this basis, interpolators from three families previously identified as promising - - piecewise-polynomial, windowed-sinc, and B-spline-based - - are compared. The results show that significant improvements over linear interpolation can straightforwardly be obtained. The recommended approach is B-spline-based interpolation, which performs best irrespective of accuracy specification. Its only drawback is a pre-filtering requirement, which represents an additional implementation cost compared to other methods. If this cost is unacceptable, and aliasing errors (on re-sampling) up to approximately 1% can be tolerated, a family of piecewise-cubic interpolators provides the best alternative.

Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging.

PubMed

Caporaso, Nicola; Whitworth, Martin B; Grebby, Stephen; Fisk, Ian D

2018-04-01

Hyperspectral imaging (HSI) is a novel technology for the food sector that enables rapid non-contact analysis of food materials. HSI was applied for the first time to whole green coffee beans, at a single seed level, for quantitative prediction of sucrose, caffeine and trigonelline content. In addition, the intra-bean distribution of coffee constituents was analysed in Arabica and Robusta coffees on a large sample set from 12 countries, using a total of 260 samples. Individual green coffee beans were scanned by reflectance HSI (980-2500nm) and then the concentration of sucrose, caffeine and trigonelline analysed with a reference method (HPLC-MS). Quantitative prediction models were subsequently built using Partial Least Squares (PLS) regression. Large variations in sucrose, caffeine and trigonelline were found between different species and origin, but also within beans from the same batch. It was shown that estimation of sucrose content is possible for screening purposes (R 2 =0.65; prediction error of ~0.7% w/w coffee, with observed range of ~6.5%), while the performance of the PLS model was better for caffeine and trigonelline prediction (R 2 =0.85 and R 2 =0.82, respectively; prediction errors of 0.2 and 0.1%, on a range of 2.3 and 1.1% w/w coffee, respectively). The prediction error is acceptable mainly for laboratory applications, with the potential application to breeding programmes and for screening purposes for the food industry. The spatial distribution of coffee constituents was also successfully visualised for single beans and this enabled mapping of the analytes across the bean structure at single pixel level. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

NASA Astrophysics Data System (ADS)

Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

2013-10-01

In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

A two-phase sampling design for increasing detections of rare species in occupancy surveys

USGS Publications Warehouse

Pacifici, Krishna; Dorazio, Robert M.; Dorazio, Michael J.

2012-01-01

1. Occupancy estimation is a commonly used tool in ecological studies owing to the ease at which data can be collected and the large spatial extent that can be covered. One major obstacle to using an occupancy-based approach is the complications associated with designing and implementing an efficient survey. These logistical challenges become magnified when working with rare species when effort can be wasted in areas with none or very few individuals. 2. Here, we develop a two-phase sampling approach that mitigates these problems by using a design that places more effort in areas with higher predicted probability of occurrence. We compare our new sampling design to traditional single-season occupancy estimation under a range of conditions and population characteristics. We develop an intuitive measure of predictive error to compare the two approaches and use simulations to assess the relative accuracy of each approach. 3. Our two-phase approach exhibited lower predictive error rates compared to the traditional single-season approach in highly spatially correlated environments. The difference was greatest when detection probability was high (0·75) regardless of the habitat or sample size. When the true occupancy rate was below 0·4 (0·05-0·4), we found that allocating 25% of the sample to the first phase resulted in the lowest error rates. 4. In the majority of scenarios, the two-phase approach showed lower error rates compared to the traditional single-season approach suggesting our new approach is fairly robust to a broad range of conditions and design factors and merits use under a wide variety of settings. 5. Synthesis and applications. Conservation and management of rare species are a challenging task facing natural resource managers. It is critical for studies involving rare species to efficiently allocate effort and resources as they are usually of a finite nature. We believe our approach provides a framework for optimal allocation of effort while maximizing the information content of the data in an attempt to provide the highest conservation value per unit of effort.

Prevalence of refractive error and visual impairment among rural school-age children of Goro District, Gurage Zone, Ethiopia.

PubMed

Kedir, Jafer; Girma, Abonesh

2014-10-01

Refractive error is one of the major causes of blindness and visual impairment in children; but community based studies are scarce especially in rural parts of Ethiopia. So, this study aims to assess the prevalence of refractive error and its magnitude as a cause of visual impairment among school-age children of rural community. This community-based cross-sectional descriptive study was conducted from March 1 to April 30, 2009 in rural villages of Goro district of Gurage Zone, found south west of Addis Ababa, the capital of Ethiopia. A multistage cluster sampling method was used with simple random selection of representative villages in the district. Chi-Square and t-tests were used in the data analysis. A total of 570 school-age children (age 7-15) were evaluated, 54% boys and 46% girls. The prevalence of refractive error was 3.5% (myopia 2.6% and hyperopia 0.9%). Refractive error was the major cause of visual impairment accounting for 54% of all causes in the study group. No child was found wearing corrective spectacles during the study period. Refractive error was the commonest cause of visual impairment in children of the district, but no measures were taken to reduce the burden in the community. So, large scale community level screening for refractive error should be conducted and integrated with regular school eye screening programs. Effective strategies need to be devised to provide low cost corrective spectacles in the rural community.

The effect of numbers of noise events on people's reactions to noise - An analysis of existing survey data

NASA Technical Reports Server (NTRS)

Fields, J. M.

1984-01-01

Even though there are surveys in which annoyance decreases as the number of events increases above about 150 a day, the available evidence is not considered strong enough to reject the conventional assumption that reactions are related to the logarithm of the number of events. The data do not make it possible to reject the conventional assumption that the effects of the number of events and the peak noise level are additive. It is found that even when equivalent questionnaire items and definitions of noise events could be used, differences between the surveys' estimates of the effect of the number of events remained large. Three explanations are suggested for inconsistent estimates. The first has to do with errors in specifying the values of noise parameters, the second with the effects of unmeasured acoustical and area characteristics that are correlated with noise level or number, and the third with large sampling errors deriving from community differences in response to noise. It is concluded that significant advances in the knowledge about the effects of the number of noise events can be made only if surveys include large numbers of study areas.

Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study

PubMed Central

Broman, Karl W.; Keller, Mark P.; Broman, Aimee Teo; Kendziorski, Christina; Yandell, Brian S.; Sen, Śaunak; Attie, Alan D.

2015-01-01

In a mouse intercross with more than 500 animals and genome-wide gene expression data on six tissues, we identified a high proportion (18%) of sample mix-ups in the genotype data. Local expression quantitative trait loci (eQTL; genetic loci influencing gene expression) with extremely large effect were used to form a classifier to predict an individual’s eQTL genotype based on expression data alone. By considering multiple eQTL and their related transcripts, we identified numerous individuals whose predicted eQTL genotypes (based on their expression data) did not match their observed genotypes, and then went on to identify other individuals whose genotypes did match the predicted eQTL genotypes. The concordance of predictions across six tissues indicated that the problem was due to mix-ups in the genotypes (although we further identified a small number of sample mix-ups in each of the six panels of gene expression microarrays). Consideration of the plate positions of the DNA samples indicated a number of off-by-one and off-by-two errors, likely the result of pipetting errors. Such sample mix-ups can be a problem in any genetic study, but eQTL data allow us to identify, and even correct, such problems. Our methods have been implemented in an R package, R/lineup. PMID:26290572

Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study.

PubMed

Broman, Karl W; Keller, Mark P; Broman, Aimee Teo; Kendziorski, Christina; Yandell, Brian S; Sen, Śaunak; Attie, Alan D

2015-08-19

In a mouse intercross with more than 500 animals and genome-wide gene expression data on six tissues, we identified a high proportion (18%) of sample mix-ups in the genotype data. Local expression quantitative trait loci (eQTL; genetic loci influencing gene expression) with extremely large effect were used to form a classifier to predict an individual's eQTL genotype based on expression data alone. By considering multiple eQTL and their related transcripts, we identified numerous individuals whose predicted eQTL genotypes (based on their expression data) did not match their observed genotypes, and then went on to identify other individuals whose genotypes did match the predicted eQTL genotypes. The concordance of predictions across six tissues indicated that the problem was due to mix-ups in the genotypes (although we further identified a small number of sample mix-ups in each of the six panels of gene expression microarrays). Consideration of the plate positions of the DNA samples indicated a number of off-by-one and off-by-two errors, likely the result of pipetting errors. Such sample mix-ups can be a problem in any genetic study, but eQTL data allow us to identify, and even correct, such problems. Our methods have been implemented in an R package, R/lineup. Copyright © 2015 Broman et al.

Error and bias in size estimates of whale sharks: implications for understanding demography.

PubMed

Sequeira, Ana M M; Thums, Michele; Brooks, Kim; Meekan, Mark G

2016-03-01

Body size and age at maturity are indicative of the vulnerability of a species to extinction. However, they are both difficult to estimate for large animals that cannot be restrained for measurement. For very large species such as whale sharks, body size is commonly estimated visually, potentially resulting in the addition of errors and bias. Here, we investigate the errors and bias associated with total lengths of whale sharks estimated visually by comparing them with measurements collected using a stereo-video camera system at Ningaloo Reef, Western Australia. Using linear mixed-effects models, we found that visual lengths were biased towards underestimation with increasing size of the shark. When using the stereo-video camera, the number of larger individuals that were possibly mature (or close to maturity) that were detected increased by approximately 10%. Mean lengths calculated by each method were, however, comparable (5.002 ± 1.194 and 6.128 ± 1.609 m, s.d.), confirming that the population at Ningaloo is mostly composed of immature sharks based on published lengths at maturity. We then collated data sets of total lengths sampled from aggregations of whale sharks worldwide between 1995 and 2013. Except for locations in the East Pacific where large females have been reported, these aggregations also largely consisted of juveniles (mean lengths less than 7 m). Sightings of the largest individuals were limited and occurred mostly prior to 2006. This result highlights the urgent need to locate and quantify the numbers of mature male and female whale sharks in order to ascertain the conservation status and ensure persistence of the species.

Sparsely sampling the sky: a Bayesian experimental design approach

NASA Astrophysics Data System (ADS)

Paykari, P.; Jaffe, A. H.

2013-08-01

The next generation of galaxy surveys will observe millions of galaxies over large volumes of the Universe. These surveys are expensive both in time and cost, raising questions regarding the optimal investment of this time and money. In this work, we investigate criteria for selecting amongst observing strategies for constraining the galaxy power spectrum and a set of cosmological parameters. Depending on the parameters of interest, it may be more efficient to observe a larger, but sparsely sampled, area of sky instead of a smaller contiguous area. In this work, by making use of the principles of Bayesian experimental design, we will investigate the advantages and disadvantages of the sparse sampling of the sky and discuss the circumstances in which a sparse survey is indeed the most efficient strategy. For the Dark Energy Survey (DES), we find that by sparsely observing the same area in a smaller amount of time, we only increase the errors on the parameters by a maximum of 0.45 per cent. Conversely, investing the same amount of time as the original DES to observe a sparser but larger area of sky, we can in fact constrain the parameters with errors reduced by 28 per cent.

The systematic component of phylogenetic error as a function of taxonomic sampling under parsimony.

PubMed

Debry, Ronald W

2005-06-01

The effect of taxonomic sampling on phylogenetic accuracy under parsimony is examined by simulating nucleotide sequence evolution. Random error is minimized by using very large numbers of simulated characters. This allows estimation of the consistency behavior of parsimony, even for trees with up to 100 taxa. Data were simulated on 8 distinct 100-taxon model trees and analyzed as stratified subsets containing either 25 or 50 taxa, in addition to the full 100-taxon data set. Overall accuracy decreased in a majority of cases when taxa were added. However, the magnitude of change in the cases in which accuracy increased was larger than the magnitude of change in the cases in which accuracy decreased, so, on average, overall accuracy increased as more taxa were included. A stratified sampling scheme was used to assess accuracy for an initial subsample of 25 taxa. The 25-taxon analyses were compared to 50- and 100-taxon analyses that were pruned to include only the original 25 taxa. On average, accuracy for the 25 taxa was improved by taxon addition, but there was considerable variation in the degree of improvement among the model trees and across different rates of substitution.

BETASEQ: a powerful novel method to control type-I error inflation in partially sequenced data for rare variant association testing.

PubMed

Yan, Song; Li, Yun

2014-02-15

Despite its great capability to detect rare variant associations, next-generation sequencing is still prohibitively expensive when applied to large samples. In case-control studies, it is thus appealing to sequence only a subset of cases to discover variants and genotype the identified variants in controls and the remaining cases under the reasonable assumption that causal variants are usually enriched among cases. However, this approach leads to inflated type-I error if analyzed naively for rare variant association. Several methods have been proposed in recent literature to control type-I error at the cost of either excluding some sequenced cases or correcting the genotypes of discovered rare variants. All of these approaches thus suffer from certain extent of information loss and thus are underpowered. We propose a novel method (BETASEQ), which corrects inflation of type-I error by supplementing pseudo-variants while keeps the original sequence and genotype data intact. Extensive simulations and real data analysis demonstrate that, in most practical situations, BETASEQ leads to higher testing powers than existing approaches with guaranteed (controlled or conservative) type-I error. BETASEQ and associated R files, including documentation, examples, are available at http://www.unc.edu/~yunmli/betaseq

Calibration of low-temperature ac susceptometers with a copper cylinder standard

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, D.-X.; Skumryev, V.

2010-02-15

A high-quality low-temperature ac susceptometer is calibrated by comparing the measured ac susceptibility of a copper cylinder with its eddy-current ac susceptibility accurately calculated. Different from conventional calibration techniques that compare the measured results with the known property of a standard sample at certain fixed temperature T, field amplitude H{sub m}, and frequency f, to get a magnitude correction factor, here, the electromagnetic properties of the copper cylinder are unknown and are determined during the calibration of the ac susceptometer in the entire T, H{sub m}, and f range. It is shown that the maximum magnitude error and the maximummore » phase error of the susceptometer are less than 0.7% and 0.3 deg., respectively, in the region T=5-300 K and f=111-1111 Hz at H{sub m}=800 A/m, after a magnitude correction by a constant factor as done in a conventional calibration. However, the magnitude and phase errors can reach 2% and 4.3 deg. at 10 000 and 11 Hz, respectively. Since the errors are reproducible, a large portion of them may be further corrected after a calibration, the procedure for which is given. Conceptual discussions concerning the error sources, comparison with other calibration methods, and applications of ac susceptibility techniques are presented.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, T. S.; DePoy, D. L.; Marshall, J. L.

Here, we report that meeting the science goals for many current and future ground-based optical large-area sky surveys requires that the calibrated broadband photometry is both stable in time and uniform over the sky to 1% precision or better. Past and current surveys have achieved photometric precision of 1%–2% by calibrating the survey's stellar photometry with repeated measurements of a large number of stars observed in multiple epochs. The calibration techniques employed by these surveys only consider the relative frame-by-frame photometric zeropoint offset and the focal plane position-dependent illumination corrections, which are independent of the source color. However, variations inmore » the wavelength dependence of the atmospheric transmission and the instrumental throughput induce source color-dependent systematic errors. These systematic errors must also be considered to achieve the most precise photometric measurements. In this paper, we examine such systematic chromatic errors (SCEs) using photometry from the Dark Energy Survey (DES) as an example. We first define a natural magnitude system for DES and calculate the systematic errors on stellar magnitudes when the atmospheric transmission and instrumental throughput deviate from the natural system. We conclude that the SCEs caused by the change of airmass in each exposure, the change of the precipitable water vapor and aerosol in the atmosphere over time, and the non-uniformity of instrumental throughput over the focal plane can be up to 2% in some bandpasses. We then compare the calculated SCEs with the observed DES data. For the test sample data, we correct these errors using measurements of the atmospheric transmission and instrumental throughput from auxiliary calibration systems. In conclusion, the residual after correction is less than 0.3%. Moreover, we calculate such SCEs for Type Ia supernovae and elliptical galaxies and find that the chromatic errors for non-stellar objects are redshift-dependent and can be larger than those for stars at certain redshifts.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, T. S.; DePoy, D. L.; Marshall, J. L.

Meeting the science goals for many current and future ground-based optical large-area sky surveys requires that the calibrated broadband photometry is both stable in time and uniform over the sky to 1% precision or better. Past and current surveys have achieved photometric precision of 1%–2% by calibrating the survey’s stellar photometry with repeated measurements of a large number of stars observed in multiple epochs. The calibration techniques employed by these surveys only consider the relative frame-by-frame photometric zeropoint offset and the focal plane position-dependent illumination corrections, which are independent of the source color. However, variations in the wavelength dependence ofmore » the atmospheric transmission and the instrumental throughput induce source color-dependent systematic errors. These systematic errors must also be considered to achieve the most precise photometric measurements. In this paper, we examine such systematic chromatic errors (SCEs) using photometry from the Dark Energy Survey (DES) as an example. We first define a natural magnitude system for DES and calculate the systematic errors on stellar magnitudes when the atmospheric transmission and instrumental throughput deviate from the natural system. We conclude that the SCEs caused by the change of airmass in each exposure, the change of the precipitable water vapor and aerosol in the atmosphere over time, and the non-uniformity of instrumental throughput over the focal plane can be up to 2% in some bandpasses. We then compare the calculated SCEs with the observed DES data. For the test sample data, we correct these errors using measurements of the atmospheric transmission and instrumental throughput from auxiliary calibration systems. The residual after correction is less than 0.3%. Moreover, we calculate such SCEs for Type Ia supernovae and elliptical galaxies and find that the chromatic errors for non-stellar objects are redshift-dependent and can be larger than those for stars at certain redshifts.« less

Error mechanism analyses of an ultra-precision stage for high speed scan motion over a large stroke

NASA Astrophysics Data System (ADS)

Wang, Shaokai; Tan, Jiubin; Cui, Jiwen

2015-02-01

Reticle Stage (RS) is designed to complete scan motion with high speed in nanometer-scale over a large stroke. Comparing with the allowable scan accuracy of a few nanometers, errors caused by any internal or external disturbances are critical and must not be ignored. In this paper, RS is firstly introduced in aspects of mechanical structure, forms of motion, and controlling method. Based on that, mechanisms of disturbances transferred to final servo-related error in scan direction are analyzed, including feedforward error, coupling between the large stroke stage (LS) and the short stroke stage (SS), and movement of measurement reference. Especially, different forms of coupling between SS and LS are discussed in detail. After theoretical analysis above, the contributions of these disturbances to final error are simulated numerically. The residual positioning error caused by feedforward error in acceleration process is about 2 nm after settling time, the coupling between SS and LS about 2.19 nm, and the movements of MF about 0.6 nm.

Fluorescent Cell Barcoding for Multiplex Flow Cytometry

PubMed Central

Krutzik, Peter O.; Clutter, Matthew R.; Trejo, Angelica; Nolan, Garry P.

2011-01-01

Fluorescent Cell Barcoding (FCB) enables high throughput, i.e. high content flow cytometry by multiplexing samples prior to staining and acquisition on the cytometer. Individual cell samples are barcoded, or labeled, with unique signatures of fluorescent dyes so that they can be mixed together, stained, and analyzed as a single sample. By mixing samples prior to staining, antibody consumption is typically reduced 10 to 100-fold. In addition, data robustness is increased through the combination of control and treated samples, which minimizes pipetting error, staining variation, and the need for normalization. Finally, speed of acquisition is enhanced, enabling large profiling experiments to be run with standard cytometer hardware. In this unit, we outline the steps necessary to apply the FCB method to cell lines as well as primary peripheral blood samples. Important technical considerations such as choice of barcoding dyes, concentrations, labeling buffers, compensation, and software analysis are discussed. PMID:21207359

An Automatic Quality Control Pipeline for High-Throughput Screening Hit Identification.

PubMed

Zhai, Yufeng; Chen, Kaisheng; Zhong, Yang; Zhou, Bin; Ainscow, Edward; Wu, Ying-Ta; Zhou, Yingyao

2016-09-01

The correction or removal of signal errors in high-throughput screening (HTS) data is critical to the identification of high-quality lead candidates. Although a number of strategies have been previously developed to correct systematic errors and to remove screening artifacts, they are not universally effective and still require fair amount of human intervention. We introduce a fully automated quality control (QC) pipeline that can correct generic interplate systematic errors and remove intraplate random artifacts. The new pipeline was first applied to ~100 large-scale historical HTS assays; in silico analysis showed auto-QC led to a noticeably stronger structure-activity relationship. The method was further tested in several independent HTS runs, where QC results were sampled for experimental validation. Significantly increased hit confirmation rates were obtained after the QC steps, confirming that the proposed method was effective in enriching true-positive hits. An implementation of the algorithm is available to the screening community. © 2016 Society for Laboratory Automation and Screening.

Real-time correction of beamforming time delay errors in abdominal ultrasound imaging

NASA Astrophysics Data System (ADS)

Rigby, K. W.

2000-04-01

The speed of sound varies with tissue type, yet commercial ultrasound imagers assume a constant sound speed. Sound speed variation in abdominal fat and muscle layers is widely believed to be largely responsible for poor contrast and resolution in some patients. The simplest model of the abdominal wall assumes that it adds a spatially varying time delay to the ultrasound wavefront. The adequacy of this model is controversial. We describe an adaptive imaging system consisting of a GE LOGIQ 700 imager connected to a multi- processor computer. Arrival time errors for each beamforming channel, estimated by correlating each channel signal with the beamsummed signal, are used to correct the imager's beamforming time delays at the acoustic frame rate. A multi- row transducer provides two-dimensional sampling of arrival time errors. We observe significant improvement in abdominal images of healthy male volunteers: increased contrast of blood vessels, increased visibility of the renal capsule, and increased brightness of the liver.

Measuring coverage in MNCH: total survey error and the interpretation of intervention coverage estimates from household surveys.

PubMed

Eisele, Thomas P; Rhoda, Dale A; Cutts, Felicity T; Keating, Joseph; Ren, Ruilin; Barros, Aluisio J D; Arnold, Fred

2013-01-01

Nationally representative household surveys are increasingly relied upon to measure maternal, newborn, and child health (MNCH) intervention coverage at the population level in low- and middle-income countries. Surveys are the best tool we have for this purpose and are central to national and global decision making. However, all survey point estimates have a certain level of error (total survey error) comprising sampling and non-sampling error, both of which must be considered when interpreting survey results for decision making. In this review, we discuss the importance of considering these errors when interpreting MNCH intervention coverage estimates derived from household surveys, using relevant examples from national surveys to provide context. Sampling error is usually thought of as the precision of a point estimate and is represented by 95% confidence intervals, which are measurable. Confidence intervals can inform judgments about whether estimated parameters are likely to be different from the real value of a parameter. We recommend, therefore, that confidence intervals for key coverage indicators should always be provided in survey reports. By contrast, the direction and magnitude of non-sampling error is almost always unmeasurable, and therefore unknown. Information error and bias are the most common sources of non-sampling error in household survey estimates and we recommend that they should always be carefully considered when interpreting MNCH intervention coverage based on survey data. Overall, we recommend that future research on measuring MNCH intervention coverage should focus on refining and improving survey-based coverage estimates to develop a better understanding of how results should be interpreted and used.

Measuring Coverage in MNCH: Total Survey Error and the Interpretation of Intervention Coverage Estimates from Household Surveys

PubMed Central

Eisele, Thomas P.; Rhoda, Dale A.; Cutts, Felicity T.; Keating, Joseph; Ren, Ruilin; Barros, Aluisio J. D.; Arnold, Fred

2013-01-01

Nationally representative household surveys are increasingly relied upon to measure maternal, newborn, and child health (MNCH) intervention coverage at the population level in low- and middle-income countries. Surveys are the best tool we have for this purpose and are central to national and global decision making. However, all survey point estimates have a certain level of error (total survey error) comprising sampling and non-sampling error, both of which must be considered when interpreting survey results for decision making. In this review, we discuss the importance of considering these errors when interpreting MNCH intervention coverage estimates derived from household surveys, using relevant examples from national surveys to provide context. Sampling error is usually thought of as the precision of a point estimate and is represented by 95% confidence intervals, which are measurable. Confidence intervals can inform judgments about whether estimated parameters are likely to be different from the real value of a parameter. We recommend, therefore, that confidence intervals for key coverage indicators should always be provided in survey reports. By contrast, the direction and magnitude of non-sampling error is almost always unmeasurable, and therefore unknown. Information error and bias are the most common sources of non-sampling error in household survey estimates and we recommend that they should always be carefully considered when interpreting MNCH intervention coverage based on survey data. Overall, we recommend that future research on measuring MNCH intervention coverage should focus on refining and improving survey-based coverage estimates to develop a better understanding of how results should be interpreted and used. PMID:23667331

Adaptive control of theophylline therapy: importance of blood sampling times.

PubMed

D'Argenio, D Z; Khakmahd, K

1983-10-01

A two-observation protocol for estimating theophylline clearance during a constant-rate intravenous infusion is used to examine the importance of blood sampling schedules with regard to the information content of resulting concentration data. Guided by a theory for calculating maximally informative sample times, population simulations are used to assess the effect of specific sampling times on the precision of resulting clearance estimates and subsequent predictions of theophylline plasma concentrations. The simulations incorporated noise terms for intersubject variability, dosing errors, sample collection errors, and assay error. Clearance was estimated using Chiou's method, least squares, and a Bayesian estimation procedure. The results of these simulations suggest that clinically significant estimation and prediction errors may result when using the above two-point protocol for estimating theophylline clearance if the time separating the two blood samples is less than one population mean elimination half-life.

High-speed adaptive contact-mode atomic force microscopy imaging with near-minimum-force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Juan; Zou, Qingze, E-mail: qzzou@rci.rutgers.edu

In this paper, an adaptive contact-mode imaging approach is proposed to replace the traditional contact-mode imaging by addressing the major concerns in both the speed and the force exerted to the sample. The speed of the traditional contact-mode imaging is largely limited by the need to maintain precision tracking of the sample topography over the entire imaged sample surface, while large image distortion and excessive probe-sample interaction force occur during high-speed imaging. In this work, first, the image distortion caused by the topography tracking error is accounted for in the topography quantification. Second, the quantified sample topography is utilized inmore » a gradient-based optimization method to adjust the cantilever deflection set-point for each scanline closely around the minimal level needed for maintaining stable probe-sample contact, and a data-driven iterative feedforward control that utilizes a prediction of the next-line topography is integrated to the topography feeedback loop to enhance the sample topography tracking. The proposed approach is demonstrated and evaluated through imaging a calibration sample of square pitches at both high speeds (e.g., scan rate of 75 Hz and 130 Hz) and large sizes (e.g., scan size of 30 μm and 80 μm). The experimental results show that compared to the traditional constant-force contact-mode imaging, the imaging speed can be increased by over 30 folds (with the scanning speed at 13 mm/s), and the probe-sample interaction force can be reduced by more than 15% while maintaining the same image quality.« less

High-speed adaptive contact-mode atomic force microscopy imaging with near-minimum-force.

PubMed

Ren, Juan; Zou, Qingze

2014-07-01

In this paper, an adaptive contact-mode imaging approach is proposed to replace the traditional contact-mode imaging by addressing the major concerns in both the speed and the force exerted to the sample. The speed of the traditional contact-mode imaging is largely limited by the need to maintain precision tracking of the sample topography over the entire imaged sample surface, while large image distortion and excessive probe-sample interaction force occur during high-speed imaging. In this work, first, the image distortion caused by the topography tracking error is accounted for in the topography quantification. Second, the quantified sample topography is utilized in a gradient-based optimization method to adjust the cantilever deflection set-point for each scanline closely around the minimal level needed for maintaining stable probe-sample contact, and a data-driven iterative feedforward control that utilizes a prediction of the next-line topography is integrated to the topography feeedback loop to enhance the sample topography tracking. The proposed approach is demonstrated and evaluated through imaging a calibration sample of square pitches at both high speeds (e.g., scan rate of 75 Hz and 130 Hz) and large sizes (e.g., scan size of 30 μm and 80 μm). The experimental results show that compared to the traditional constant-force contact-mode imaging, the imaging speed can be increased by over 30 folds (with the scanning speed at 13 mm/s), and the probe-sample interaction force can be reduced by more than 15% while maintaining the same image quality.

Sampling design for spatially distributed hydrogeologic and environmental processes

USGS Publications Warehouse

Christakos, G.; Olea, R.A.

1992-01-01

A methodology for the design of sampling networks over space is proposed. The methodology is based on spatial random field representations of nonhomogeneous natural processes, and on optimal spatial estimation techniques. One of the most important results of random field theory for physical sciences is its rationalization of correlations in spatial variability of natural processes. This correlation is extremely important both for interpreting spatially distributed observations and for predictive performance. The extent of site sampling and the types of data to be collected will depend on the relationship of subsurface variability to predictive uncertainty. While hypothesis formulation and initial identification of spatial variability characteristics are based on scientific understanding (such as knowledge of the physics of the underlying phenomena, geological interpretations, intuition and experience), the support offered by field data is statistically modelled. This model is not limited by the geometric nature of sampling and covers a wide range in subsurface uncertainties. A factorization scheme of the sampling error variance is derived, which possesses certain atttactive properties allowing significant savings in computations. By means of this scheme, a practical sampling design procedure providing suitable indices of the sampling error variance is established. These indices can be used by way of multiobjective decision criteria to obtain the best sampling strategy. Neither the actual implementation of the in-situ sampling nor the solution of the large spatial estimation systems of equations are necessary. The required values of the accuracy parameters involved in the network design are derived using reference charts (readily available for various combinations of data configurations and spatial variability parameters) and certain simple yet accurate analytical formulas. Insight is gained by applying the proposed sampling procedure to realistic examples related to sampling problems in two dimensions. ?? 1992.

Increasing point-count duration increases standard error

USGS Publications Warehouse

Smith, W.P.; Twedt, D.J.; Hamel, P.B.; Ford, R.P.; Wiedenfeld, D.A.; Cooper, R.J.

1998-01-01

We examined data from point counts of varying duration in bottomland forests of west Tennessee and the Mississippi Alluvial Valley to determine if counting interval influenced sampling efficiency. Estimates of standard error increased as point count duration increased both for cumulative number of individuals and species in both locations. Although point counts appear to yield data with standard errors proportional to means, a square root transformation of the data may stabilize the variance. Using long (>10 min) point counts may reduce sample size and increase sampling error, both of which diminish statistical power and thereby the ability to detect meaningful changes in avian populations.

Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood

PubMed Central

Bondell, Howard D.; Stefanski, Leonard A.

2013-01-01

Large- and finite-sample efficiency and resistance to outliers are the key goals of robust statistics. Although often not simultaneously attainable, we develop and study a linear regression estimator that comes close. Efficiency obtains from the estimator’s close connection to generalized empirical likelihood, and its favorable robustness properties are obtained by constraining the associated sum of (weighted) squared residuals. We prove maximum attainable finite-sample replacement breakdown point, and full asymptotic efficiency for normal errors. Simulation evidence shows that compared to existing robust regression estimators, the new estimator has relatively high efficiency for small sample sizes, and comparable outlier resistance. The estimator is further illustrated and compared to existing methods via application to a real data set with purported outliers. PMID:23976805

On using summary statistics from an external calibration sample to correct for covariate measurement error.

PubMed

Guo, Ying; Little, Roderick J; McConnell, Daniel S

2012-01-01

Covariate measurement error is common in epidemiologic studies. Current methods for correcting measurement error with information from external calibration samples are insufficient to provide valid adjusted inferences. We consider the problem of estimating the regression of an outcome Y on covariates X and Z, where Y and Z are observed, X is unobserved, but a variable W that measures X with error is observed. Information about measurement error is provided in an external calibration sample where data on X and W (but not Y and Z) are recorded. We describe a method that uses summary statistics from the calibration sample to create multiple imputations of the missing values of X in the regression sample, so that the regression coefficients of Y on X and Z and associated standard errors can be estimated using simple multiple imputation combining rules, yielding valid statistical inferences under the assumption of a multivariate normal distribution. The proposed method is shown by simulation to provide better inferences than existing methods, namely the naive method, classical calibration, and regression calibration, particularly for correction for bias and achieving nominal confidence levels. We also illustrate our method with an example using linear regression to examine the relation between serum reproductive hormone concentrations and bone mineral density loss in midlife women in the Michigan Bone Health and Metabolism Study. Existing methods fail to adjust appropriately for bias due to measurement error in the regression setting, particularly when measurement error is substantial. The proposed method corrects this deficiency.

Efficient preparation of large-block-code ancilla states for fault-tolerant quantum computation

NASA Astrophysics Data System (ADS)

Zheng, Yi-Cong; Lai, Ching-Yi; Brun, Todd A.

2018-03-01

Fault-tolerant quantum computation (FTQC) schemes that use multiqubit large block codes can potentially reduce the resource overhead to a great extent. A major obstacle is the requirement for a large number of clean ancilla states of different types without correlated errors inside each block. These ancilla states are usually logical stabilizer states of the data-code blocks, which are generally difficult to prepare if the code size is large. Previously, we have proposed an ancilla distillation protocol for Calderbank-Shor-Steane (CSS) codes by classical error-correcting codes. It was assumed that the quantum gates in the distillation circuit were perfect; however, in reality, noisy quantum gates may introduce correlated errors that are not treatable by the protocol. In this paper, we show that additional postselection by another classical error-detecting code can be applied to remove almost all correlated errors. Consequently, the revised protocol is fully fault tolerant and capable of preparing a large set of stabilizer states sufficient for FTQC using large block codes. At the same time, the yield rate can be boosted from O (t-2) to O (1 ) in practice for an [[n ,k ,d =2 t +1

A Dual Frequency Carrier Phase Error Difference Checking Algorithm for the GNSS Compass.

PubMed

Liu, Shuo; Zhang, Lei; Li, Jian

2016-11-24

The performance of the Global Navigation Satellite System (GNSS) compass is related to the quality of carrier phase measurement. How to process the carrier phase error properly is important to improve the GNSS compass accuracy. In this work, we propose a dual frequency carrier phase error difference checking algorithm for the GNSS compass. The algorithm aims at eliminating large carrier phase error in dual frequency double differenced carrier phase measurement according to the error difference between two frequencies. The advantage of the proposed algorithm is that it does not need additional environment information and has a good performance on multiple large errors compared with previous research. The core of the proposed algorithm is removing the geographical distance from the dual frequency carrier phase measurement, then the carrier phase error is separated and detectable. We generate the Double Differenced Geometry-Free (DDGF) measurement according to the characteristic that the different frequency carrier phase measurements contain the same geometrical distance. Then, we propose the DDGF detection to detect the large carrier phase error difference between two frequencies. The theoretical performance of the proposed DDGF detection is analyzed. An open sky test, a manmade multipath test and an urban vehicle test were carried out to evaluate the performance of the proposed algorithm. The result shows that the proposed DDGF detection is able to detect large error in dual frequency carrier phase measurement by checking the error difference between two frequencies. After the DDGF detection, the accuracy of the baseline vector is improved in the GNSS compass.

Evaluation of process errors in bed load sampling using a Dune Model

USGS Publications Warehouse

Gomez, Basil; Troutman, Brent M.

1997-01-01

Reliable estimates of the streamwide bed load discharge obtained using sampling devices are dependent upon good at-a-point knowledge across the full width of the channel. Using field data and information derived from a model that describes the geometric features of a dune train in terms of a spatial process observed at a fixed point in time, we show that sampling errors decrease as the number of samples collected increases, and the number of traverses of the channel over which the samples are collected increases. It also is preferable that bed load sampling be conducted at a pace which allows a number of bed forms to pass through the sampling cross section. The situations we analyze and simulate pertain to moderate transport conditions in small rivers. In such circumstances, bed load sampling schemes typically should involve four or five traverses of a river, and the collection of 20–40 samples at a rate of five or six samples per hour. By ensuring that spatial and temporal variability in the transport process is accounted for, such a sampling design reduces both random and systematic errors and hence minimizes the total error involved in the sampling process.

Large-volume injection of sample diluents not miscible with the mobile phase as an alternative approach in sample preparation for bioanalysis: an application for fenspiride bioequivalence.

PubMed

Medvedovici, Andrei; Udrescu, Stefan; Albu, Florin; Tache, Florentin; David, Victor

2011-09-01

Liquid-liquid extraction of target compounds from biological matrices followed by the injection of a large volume from the organic layer into the chromatographic column operated under reversed-phase (RP) conditions would successfully combine the selectivity and the straightforward character of the procedure in order to enhance sensitivity, compared with the usual approach of involving solvent evaporation and residue re-dissolution. Large-volume injection of samples in diluents that are not miscible with the mobile phase was recently introduced in chromatographic practice. The risk of random errors produced during the manipulation of samples is also substantially reduced. A bioanalytical method designed for the bioequivalence of fenspiride containing pharmaceutical formulations was based on a sample preparation procedure involving extraction of the target analyte and the internal standard (trimetazidine) from alkalinized plasma samples in 1-octanol. A volume of 75 µl from the octanol layer was directly injected on a Zorbax SB C18 Rapid Resolution, 50 mm length × 4.6 mm internal diameter × 1.8 µm particle size column, with the RP separation being carried out under gradient elution conditions. Detection was made through positive ESI and MS/MS. Aspects related to method development and validation are discussed. The bioanalytical method was successfully applied to assess bioequivalence of a modified release pharmaceutical formulation containing 80 mg fenspiride hydrochloride during two different studies carried out as single-dose administration under fasting and fed conditions (four arms), and multiple doses administration, respectively. The quality attributes assigned to the bioanalytical method, as resulting from its application to the bioequivalence studies, are highlighted and fully demonstrate that sample preparation based on large-volume injection of immiscible diluents has an increased potential for application in bioanalysis.

Sampling Error in a Particulate Mixture: An Analytical Chemistry Experiment.

ERIC Educational Resources Information Center

Kratochvil, Byron

1980-01-01

Presents an undergraduate experiment demonstrating sampling error. Selected as the sampling system is a mixture of potassium hydrogen phthalate and sucrose; using a self-zeroing, automatically refillable buret to minimize titration time of multiple samples and employing a dilute back-titrant to obtain high end-point precision. (CS)

Thermal Conductivities of Some Polymers and Composites

DTIC Science & Technology

2018-02-01

volume fraction of glass and fabric style. The experimental results are compared to modeled results for Kt in composites. 15. SUBJECT TERMS...entities in a polymer above TG increases, so Cp will increase at TG. For Kt to remain constant, there would have to be a comparable decrease in α due to...scanning calorimetry (DSC) method, and have error bars as large as the claimed effect. Their Kt values for their carbon fiber samples are comparable to

The VIMOS Public Extragalactic Redshift Survey (VIPERS). An unbiased estimate of the growth rate of structure at ⟨z⟩ = 0.85 using the clustering of luminous blue galaxies

NASA Astrophysics Data System (ADS)

Mohammad, F. G.; Granett, B. R.; Guzzo, L.; Bel, J.; Branchini, E.; de la Torre, S.; Moscardini, L.; Peacock, J. A.; Bolzonella, M.; Garilli, B.; Scodeggio, M.; Abbas, U.; Adami, C.; Bottini, D.; Cappi, A.; Cucciati, O.; Davidzon, I.; Franzetti, P.; Fritz, A.; Iovino, A.; Krywult, J.; Le Brun, V.; Le Fèvre, O.; Maccagni, D.; Małek, K.; Marulli, F.; Polletta, M.; Pollo, A.; Tasca, L. A. M.; Tojeiro, R.; Vergani, D.; Zanichelli, A.; Arnouts, S.; Coupon, J.; De Lucia, G.; Ilbert, O.; Moutard, T.

2018-02-01

We used the VIMOS Public Extragalactic Redshift Survey (VIPERS) final data release (PDR-2) to investigate the performance of colour-selected populations of galaxies as tracers of linear large-scale motions. We empirically selected volume-limited samples of blue and red galaxies as to minimise the systematic error on the estimate of the growth rate of structure fσ8 from the anisotropy of the two-point correlation function. To this end, rather than rigidly splitting the sample into two colour classes we defined the red or blue fractional contribution of each object through a weight based on the (U - V ) colour distribution. Using mock surveys that are designed to reproduce the observed properties of VIPERS galaxies, we find the systematic error in recovering the fiducial value of fσ8 to be minimised when using a volume-limited sample of luminous blue galaxies. We modelled non-linear corrections via the Scoccimarro extension of the Kaiser model (with updated fitting formulae for the velocity power spectra), finding systematic errors on fσ8 of below 1-2%, using scales as small as 5 h-1 Mpc. We interpret this result as indicating that selection of luminous blue galaxies maximises the fraction that are central objects in their dark matter haloes; this in turn minimises the contribution to the measured ξ(rp,π) from the 1-halo term, which is dominated by non-linear motions. The gain is inferior if one uses the full magnitude-limited sample of blue objects, consistent with the presence of a significant fraction of blue, fainter satellites dominated by non-streaming, orbital velocities. We measured a value of fσ8 = 0.45 ± 0.11 over the single redshift range 0.6 ≤ z ≤ 1.0, corresponding to an effective redshift for the blue galaxies ⟨z⟩=0.85. Including in the likelihood the potential extra information contained in the blue-red galaxy cross-correlation function does not lead to an appreciable improvement in the error bars, while it increases the systematic error. Based on observations collected at the European Southern Observatory, Cerro Paranal, Chile, using the Very Large Telescope under programs 182.A-0886 and partly 070.A-9007. Also based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT), which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is based in part on data products produced at TERAPIX and the Canadian Astronomy Data Centre as part of the Canada-France-Hawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS. The VIPERS web site is http://www.vipers.inaf.it/

Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

NASA Technical Reports Server (NTRS)

Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

2013-01-01

The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

Role of training activities for the reduction of pre-analytical errors in laboratory samples from primary care.

PubMed

Romero, Adolfo; Cobos, Andrés; Gómez, Juan; Muñoz, Manuel

2012-01-18

The presence of pre-analytical errors (PE) is a usual contingency in laboratories. The incidence may increase where it is difficult to control that period, as it is the case with samples sent from primary care (PC) to clinical reference laboratory. Detection of a large number of PE in PC samples in our Institution led to the development and implementation of preventive strategies. The first of these has been the realization of a cycle of educational sessions for PC nurses, followed by the evaluation of their impact on PE number. The incidence of PE was assessed in two periods, before (October-November 2007) and after (October-November, 2009) the implementation of educational sessions. Eleven PC centers in the urban area and 17 in the rural area participated. In the urban area, samples were withdrawn by any PC nurse; in the rural area, samples were obtained by the patient's reference nurse. The types of analyzed PE included missed sample (MS), hemolyzed sample (HS), coagulated sample (CS), incorrect sample (ISV) and others (OPE), such as lipemic or icteric serum or plasma. In the former period, we received 52,669 blood samples and 18,852 urine samples, detecting 3885 (7.5%) and 1567 (8.3%) PEs, respectively. After the educational intervention, there were 52,659 and 19,048 samples with 5057 (9.6%) and 1.256 (6.5%) PEs, respectively (p<0.001). According to the type of PE, the incidents compared before and after compared incidences were: MS, 4.8% vs. 3.8%, p<0.001; HS, 1.97% vs. 3.9%, p<0.001; CS, 0.54% vs. 0.25%, p<0.001; ISV, 0.15% vs. 0.19% p=0.08; and OPE, 0.3% vs. 0.42%, p<0.001. Surprisingly the PE incidence increased after the educational intervention, although it should be noted that it was primarily due to the increase of HS, as the other EP incidence decreased (MS and CS) or remained unchanged (ISV). This seems to indicate the need for a comprehensive approach to reduce the incidence of errors in the pre-analytical period, as one stage interventions do not seem to be effective enough. Copyright © 2011 Elsevier B.V. All rights reserved.

Estimating tree biomass regressions and their error, proceedings of the workshop on tree biomass regression functions and their contribution to the error

Treesearch

Eric H. Wharton; Tiberius Cunia

1987-01-01

Proceedings of a workshop co-sponsored by the USDA Forest Service, the State University of New York, and the Society of American Foresters. Presented were papers on the methodology of sample tree selection, tree biomass measurement, construction of biomass tables and estimation of their error, and combining the error of biomass tables with that of the sample plots or...

Colour coding for blood collection tube closures - a call for harmonisation.

PubMed

Simundic, Ana-Maria; Cornes, Michael P; Grankvist, Kjell; Lippi, Giuseppe; Nybo, Mads; Ceriotti, Ferruccio; Theodorsson, Elvar; Panteghini, Mauro

2015-02-01

At least one in 10 patients experience adverse events while receiving hospital care. Many of the errors are related to laboratory diagnostics. Efforts to reduce laboratory errors over recent decades have primarily focused on the measurement process while pre- and post-analytical errors including errors in sampling, reporting and decision-making have received much less attention. Proper sampling and additives to the samples are essential. Tubes and additives are identified not only in writing on the tubes but also by the colour of the tube closures. Unfortunately these colours have not been standardised, running the risk of error when tubes from one manufacturer are replaced by the tubes from another manufacturer that use different colour coding. EFLM therefore supports the worldwide harmonisation of the colour coding for blood collection tube closures and labels in order to reduce the risk of pre-analytical errors and improve the patient safety.

Risks of Large Portfolios

PubMed Central

Fan, Jianqing; Liao, Yuan; Shi, Xiaofeng

2014-01-01

The risk of a large portfolio is often estimated by substituting a good estimator of the volatility matrix. However, the accuracy of such a risk estimator is largely unknown. We study factor-based risk estimators under a large amount of assets, and introduce a high-confidence level upper bound (H-CLUB) to assess the estimation. The H-CLUB is constructed using the confidence interval of risk estimators with either known or unknown factors. We derive the limiting distribution of the estimated risks in high dimensionality. We find that when the dimension is large, the factor-based risk estimators have the same asymptotic variance no matter whether the factors are known or not, which is slightly smaller than that of the sample covariance-based estimator. Numerically, H-CLUB outperforms the traditional crude bounds, and provides an insightful risk assessment. In addition, our simulated results quantify the relative error in the risk estimation, which is usually negligible using 3-month daily data. PMID:26195851

Radar error statistics for the space shuttle

NASA Technical Reports Server (NTRS)

Lear, W. M.

1979-01-01

Radar error statistics of C-band and S-band that are recommended for use with the groundtracking programs to process space shuttle tracking data are presented. The statistics are divided into two parts: bias error statistics, using the subscript B, and high frequency error statistics, using the subscript q. Bias errors may be slowly varying to constant. High frequency random errors (noise) are rapidly varying and may or may not be correlated from sample to sample. Bias errors were mainly due to hardware defects and to errors in correction for atmospheric refraction effects. High frequency noise was mainly due to hardware and due to atmospheric scintillation. Three types of atmospheric scintillation were identified: horizontal, vertical, and line of sight. This was the first time that horizontal and line of sight scintillations were identified.

Mass load estimation errors utilizing grab sampling strategies in a karst watershed

USGS Publications Warehouse

Fogle, A.W.; Taraba, J.L.; Dinger, J.S.

2003-01-01

Developing a mass load estimation method appropriate for a given stream and constituent is difficult due to inconsistencies in hydrologic and constituent characteristics. The difficulty may be increased in flashy flow conditions such as karst. Many projects undertaken are constrained by budget and manpower and do not have the luxury of sophisticated sampling strategies. The objectives of this study were to: (1) examine two grab sampling strategies with varying sampling intervals and determine the error in mass load estimates, and (2) determine the error that can be expected when a grab sample is collected at a time of day when the diurnal variation is most divergent from the daily mean. Results show grab sampling with continuous flow to be a viable data collection method for estimating mass load in the study watershed. Comparing weekly, biweekly, and monthly grab sampling, monthly sampling produces the best results with this method. However, the time of day the sample is collected is important. Failure to account for diurnal variability when collecting a grab sample may produce unacceptable error in mass load estimates. The best time to collect a sample is when the diurnal cycle is nearest the daily mean.

Field evaluation of distance-estimation error during wetland-dependent bird surveys

USGS Publications Warehouse

Nadeau, Christopher P.; Conway, Courtney J.

2012-01-01

Context: The most common methods to estimate detection probability during avian point-count surveys involve recording a distance between the survey point and individual birds detected during the survey period. Accurately measuring or estimating distance is an important assumption of these methods; however, this assumption is rarely tested in the context of aural avian point-count surveys. Aims: We expand on recent bird-simulation studies to document the error associated with estimating distance to calling birds in a wetland ecosystem. Methods: We used two approaches to estimate the error associated with five surveyor's distance estimates between the survey point and calling birds, and to determine the factors that affect a surveyor's ability to estimate distance. Key results: We observed biased and imprecise distance estimates when estimating distance to simulated birds in a point-count scenario (x̄error = -9 m, s.d.error = 47 m) and when estimating distances to real birds during field trials (x̄error = 39 m, s.d.error = 79 m). The amount of bias and precision in distance estimates differed among surveyors; surveyors with more training and experience were less biased and more precise when estimating distance to both real and simulated birds. Three environmental factors were important in explaining the error associated with distance estimates, including the measured distance from the bird to the surveyor, the volume of the call and the species of bird. Surveyors tended to make large overestimations to birds close to the survey point, which is an especially serious error in distance sampling. Conclusions: Our results suggest that distance-estimation error is prevalent, but surveyor training may be the easiest way to reduce distance-estimation error. Implications: The present study has demonstrated how relatively simple field trials can be used to estimate the error associated with distance estimates used to estimate detection probability during avian point-count surveys. Evaluating distance-estimation errors will allow investigators to better evaluate the accuracy of avian density and trend estimates. Moreover, investigators who evaluate distance-estimation errors could employ recently developed models to incorporate distance-estimation error into analyses. We encourage further development of such models, including the inclusion of such models into distance-analysis software.

SAMPLING DISTRIBUTIONS OF ERROR IN MULTIDIMENSIONAL SCALING.

ERIC Educational Resources Information Center

STAKE, ROBERT E.; AND OTHERS

AN EMPIRICAL STUDY WAS MADE OF THE ERROR FACTORS IN MULTIDIMENSIONAL SCALING (MDS) TO REFINE THE USE OF MDS FOR MORE EXPERT MANIPULATION OF SCALES USED IN EDUCATIONAL MEASUREMENT. THE PURPOSE OF THE RESEARCH WAS TO GENERATE TABLES OF THE SAMPLING DISTRIBUTIONS THAT ARE NECESSARY FOR DISCRIMINATING BETWEEN ERROR AND NONERROR MDS DIMENSIONS. THE…

Error Identification, Labeling, and Correction in Written Business Communication

ERIC Educational Resources Information Center

Quible, Zane K.

2004-01-01

This article used a writing sample that contained 27 sentence-level errors of the type found by corporate America to be annoying and bothersome. Five categories of errors were included in the sample: grammar, punctuation, spelling, writing style, and business communication concepts. Students in a written business communication course were asked…

Hospitalized Patients' Perceptions of Resident Fatigue, Duty Hours, and Continuity of Care.

PubMed

Drolet, Brian C; Hyman, Charles H; Ghaderi, Kimeya F; Rodriguez-Srednicki, Joshua; Thompson, Jordan M; Fischer, Staci A

2014-12-01

Physicians' perceptions of duty hour regulations have been closely examined, yet patient opinions have been largely unstudied to date. We studied patient perceptions of residency duty hours, fatigue, and continuity of care following implementation of the Accreditation Council for Graduate Medical Education 2011 Common Program Requirements. A cross-sectional survey was administered between June and August 2013 to inpatients at a large academic medical center and an affiliated community hospital. Adult inpatients on teaching medical and surgical services were eligible for inclusion in the study. Survey response rate was 71.3% (513 of 720). Most respondents (57.1%, 293 of 513) believed residents should not be assigned to shifts longer than 12 hours, and nearly half (49.7%, 255 of 513) wanted to be notified if a resident caring for them had worked longer than 12 hours. Most patients (63.2%, 324 of 513) believed medical errors commonly occurred because of fatigue, and fewer (37.4%, 192 of 513; odds ratio, 0.56; P < .01) believed medical errors commonly occurred as a result of transfers of care. Given the choice between a familiar physician who "may be tired from a long shift" or a "fresh" physician who had received sign-out, more patients chose the fresh but unfamiliar physician (57.1% [293 of 513] versus 42.7% [219 of 513], P < .01). In a survey about physician attributes relevant to medical errors and patient safety, adult inpatients in a large and diverse sample reported greater concern about fatigue and working hours than about continuity of care.

Mesoscale Predictability and Error Growth in Short Range Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Gingrich, Mark

Although it was originally suggested that small-scale, unresolved errors corrupt forecasts at all scales through an inverse error cascade, some authors have proposed that those mesoscale circulations resulting from stationary forcing on the larger scale may inherit the predictability of the large-scale motions. Further, the relative contributions of large- and small-scale uncertainties in producing error growth in the mesoscales remain largely unknown. Here, 100 member ensemble forecasts are initialized from an ensemble Kalman filter (EnKF) to simulate two winter storms impacting the East Coast of the United States in 2010. Four verification metrics are considered: the local snow water equivalence, total liquid water, and 850 hPa temperatures representing mesoscale features; and the sea level pressure field representing a synoptic feature. It is found that while the predictability of the mesoscale features can be tied to the synoptic forecast, significant uncertainty existed on the synoptic scale at lead times as short as 18 hours. Therefore, mesoscale details remained uncertain in both storms due to uncertainties at the large scale. Additionally, the ensemble perturbation kinetic energy did not show an appreciable upscale propagation of error for either case. Instead, the initial condition perturbations from the cycling EnKF were maximized at large scales and immediately amplified at all scales without requiring initial upscale propagation. This suggests that relatively small errors in the synoptic-scale initialization may have more importance in limiting predictability than errors in the unresolved, small-scale initial conditions.

Statistical inference involving binomial and negative binomial parameters.

PubMed

García-Pérez, Miguel A; Núñez-Antón, Vicente

2009-05-01

Statistical inference about two binomial parameters implies that they are both estimated by binomial sampling. There are occasions in which one aims at testing the equality of two binomial parameters before and after the occurrence of the first success along a sequence of Bernoulli trials. In these cases, the binomial parameter before the first success is estimated by negative binomial sampling whereas that after the first success is estimated by binomial sampling, and both estimates are related. This paper derives statistical tools to test two hypotheses, namely, that both binomial parameters equal some specified value and that both parameters are equal though unknown. Simulation studies are used to show that in small samples both tests are accurate in keeping the nominal Type-I error rates, and also to determine sample size requirements to detect large, medium, and small effects with adequate power. Additional simulations also show that the tests are sufficiently robust to certain violations of their assumptions.

Nematode Damage Functions: The Problems of Experimental and Sampling Error

PubMed Central

Ferris, H.

1984-01-01

The development and use of pest damage functions involves measurement and experimental errors associated with cultural, environmental, and distributional factors. Damage predictions are more valuable if considered with associated probability. Collapsing population densities into a geometric series of population classes allows a pseudo-replication removal of experimental and sampling error in damage function development. Recognition of the nature of sampling error for aggregated populations allows assessment of probability associated with the population estimate. The product of the probabilities incorporated in the damage function and in the population estimate provides a basis for risk analysis of the yield loss prediction and the ensuing management decision. PMID:19295865

A Comprehensive review of group level model performance in the presence of heteroscedasticity: Can a single model control Type I errors in the presence of outliers?

PubMed Central

Mumford, Jeanette A.

2017-01-01

Even after thorough preprocessing and a careful time series analysis of functional magnetic resonance imaging (fMRI) data, artifact and other issues can lead to violations of the assumption that the variance is constant across subjects in the group level model. This is especially concerning when modeling a continuous covariate at the group level, as the slope is easily biased by outliers. Various models have been proposed to deal with outliers including models that use the first level variance or that use the group level residual magnitude to differentially weight subjects. The most typically used robust regression, implementing a robust estimator of the regression slope, has been previously studied in the context of fMRI studies and was found to perform well in some scenarios, but a loss of Type I error control can occur for some outlier settings. A second type of robust regression using a heteroscedastic autocorrelation consistent (HAC) estimator, which produces robust slope and variance estimates has been shown to perform well, with better Type I error control, but with large sample sizes (500–1000 subjects). The Type I error control with smaller sample sizes has not been studied in this model and has not been compared to other modeling approaches that handle outliers such as FSL’s Flame 1 and FSL’s outlier de-weighting. Focusing on group level inference with a continuous covariate over a range of sample sizes and degree of heteroscedasticity, which can be driven either by the within- or between-subject variability, both styles of robust regression are compared to ordinary least squares (OLS), FSL’s Flame 1, Flame 1 with outlier de-weighting algorithm and Kendall’s Tau. Additionally, subject omission using the Cook’s Distance measure with OLS and nonparametric inference with the OLS statistic are studied. Pros and cons of these models as well as general strategies for detecting outliers in data and taking precaution to avoid inflated Type I error rates are discussed. PMID:28030782

Validation of Metrics as Error Predictors

NASA Astrophysics Data System (ADS)

Mendling, Jan

In this chapter, we test the validity of metrics that were defined in the previous chapter for predicting errors in EPC business process models. In Section 5.1, we provide an overview of how the analysis data is generated. Section 5.2 describes the sample of EPCs from practice that we use for the analysis. Here we discuss a disaggregation by the EPC model group and by error as well as a correlation analysis between metrics and error. Based on this sample, we calculate a logistic regression model for predicting error probability with the metrics as input variables in Section 5.3. In Section 5.4, we then test the regression function for an independent sample of EPC models from textbooks as a cross-validation. Section 5.5 summarizes the findings.

Spatial sampling considerations of the CERES (Clouds and Earth Radiant Energy System) instrument

NASA Astrophysics Data System (ADS)

Smith, G. L.; Manalo-Smith, Natividdad; Priestley, Kory

2014-10-01

The CERES (Clouds and Earth Radiant Energy System) instrument is a scanning radiometer with three channels for measuring Earth radiation budget. At present CERES models are operating aboard the Terra, Aqua and Suomi/NPP spacecraft and flights of CERES instruments are planned for the JPSS-1 spacecraft and its successors. CERES scans from one limb of the Earth to the other and back. The footprint size grows with distance from nadir simply due to geometry so that the size of the smallest features which can be resolved from the data increases and spatial sampling errors increase with nadir angle. This paper presents an analysis of the effect of nadir angle on spatial sampling errors of the CERES instrument. The analysis performed in the Fourier domain. Spatial sampling errors are created by smoothing of features which are the size of the footprint and smaller, or blurring, and inadequate sampling, that causes aliasing errors. These spatial sampling errors are computed in terms of the system transfer function, which is the Fourier transform of the point response function, the spacing of data points and the spatial spectrum of the radiance field.

Sampling methods for titica vine (Heteropsis spp.) inventory in a tropical forest

Treesearch

Carine Klauberg; Edson Vidal; Carlos Alberto Silva; Michelliny de M. Bentes; Andrew Thomas Hudak

2016-01-01

Titica vine provides useful raw fiber material. Using sampling schemes that reduce sampling error can provide direction for sustainable forest management of this vine. Sampling systematically with rectangular plots (10× 25 m) promoted lower error and greater accuracy in the inventory of titica vines in tropical rainforest.

Distribution of the Determinant of the Sample Correlation Matrix: Monte Carlo Type One Error Rates.

ERIC Educational Resources Information Center

Reddon, John R.; And Others

1985-01-01

Computer sampling from a multivariate normal spherical population was used to evaluate the type one error rates for a test of sphericity based on the distribution of the determinant of the sample correlation matrix. (Author/LMO)

Understanding the dynamics of correct and error responses in free recall: evidence from externalized free recall.

PubMed

Unsworth, Nash; Brewer, Gene A; Spillers, Gregory J

2010-06-01

The dynamics of correct and error responses in a variant of delayed free recall were examined in the present study. In the externalized free recall paradigm, participants were presented with lists of words and were instructed to subsequently recall not only the words that they could remember from the most recently presented list, but also any other words that came to mind during the recall period. Externalized free recall is useful for elucidating both sampling and postretrieval editing processes, thereby yielding more accurate estimates of the total number of error responses, which are typically sampled and subsequently edited during free recall. The results indicated that the participants generally sampled correct items early in the recall period and then transitioned to sampling more erroneous responses. Furthermore, the participants generally terminated their search after sampling too many errors. An examination of editing processes suggested that the participants were quite good at identifying errors, but this varied systematically on the basis of a number of factors. The results from the present study are framed in terms of generate-edit models of free recall.

Mental representation of symbols as revealed by vocabulary errors in two bonobos (Pan paniscus).

PubMed

Lyn, Heidi

2007-10-01

Error analysis has been used in humans to detect implicit representations and categories in language use. The present study utilizes the same technique to report on mental representations and categories in symbol use from two bonobos (Pan paniscus). These bonobos have been shown in published reports to comprehend English at the level of a two-and-a-half year old child and to use a keyboard with over 200 visuographic symbols (lexigrams). In this study, vocabulary test errors from over 10 years of data revealed auditory, visual, and spatio-temporal generalizations (errors were more likely items that looked like sounded like, or were frequently associated with the sample item in space or in time), as well as hierarchical and conceptual categorizations. These error data, like those of humans, are a result of spontaneous responding rather than specific training and do not solely depend upon the sample mode (e.g. auditory similarity errors are not universally more frequent with an English sample, nor were visual similarity errors universally more frequent with a photograph sample). However, unlike humans, these bonobos do not make errors based on syntactical confusions (e.g. confusing semantically unrelated nouns), suggesting that they may not separate syntactical and semantic information. These data suggest that apes spontaneously create a complex, hierarchical, web of representations when exposed to a symbol system.

Evaluation of Preanalytical Quality Indicators by Six Sigma and Pareto`s Principle.

PubMed

Kulkarni, Sweta; Ramesh, R; Srinivasan, A R; Silvia, C R Wilma Delphine

2018-01-01

Preanalytical steps are the major sources of error in clinical laboratory. The analytical errors can be corrected by quality control procedures but there is a need for stringent quality checks in preanalytical area as these processes are done outside the laboratory. Sigma value depicts the performance of laboratory and its quality measures. Hence in the present study six sigma and Pareto principle was applied to preanalytical quality indicators to evaluate the clinical biochemistry laboratory performance. This observational study was carried out for a period of 1 year from November 2015-2016. A total of 1,44,208 samples and 54,265 test requisition forms were screened for preanalytical errors like missing patient information, sample collection details in forms and hemolysed, lipemic, inappropriate, insufficient samples and total number of errors were calculated and converted into defects per million and sigma scale. Pareto`s chart was drawn using total number of errors and cumulative percentage. In 75% test requisition forms diagnosis was not mentioned and sigma value of 0.9 was obtained and for other errors like sample receiving time, stat and type of sample sigma values were 2.9, 2.6, and 2.8 respectively. For insufficient sample and improper ratio of blood to anticoagulant sigma value was 4.3. Pareto`s chart depicts out of 80% of errors in requisition forms, 20% is contributed by missing information like diagnosis. The development of quality indicators, application of six sigma and Pareto`s principle are quality measures by which not only preanalytical, the total testing process can be improved.

Error assessment in molecular dynamics trajectories using computed NMR chemical shifts.

PubMed

Koes, David R; Vries, John K

2017-01-01

Accurate chemical shifts for the atoms in molecular mechanics (MD) trajectories can be obtained from quantum mechanical (QM) calculations that depend solely on the coordinates of the atoms in the localized regions surrounding atoms of interest. If these coordinates are correct and the sample size is adequate, the ensemble average of these chemical shifts should be equal to the chemical shifts obtained from NMR spectroscopy. If this is not the case, the coordinates must be incorrect. We have utilized this fact to quantify the errors associated with the backbone atoms in MD simulations of proteins. A library of regional conformers containing 169,499 members was constructed from 6 model proteins. The chemical shifts associated with the backbone atoms in each of these conformers was obtained from QM calculations using density functional theory at the B3LYP level with a 6-311+G(2d,p) basis set. Chemical shifts were assigned to each backbone atom in each MD simulation frame using a template matching approach. The ensemble average of these chemical shifts was compared to chemical shifts from NMR spectroscopy. A large systematic error was identified that affected the 1 H atoms of the peptide bonds involved in hydrogen bonding with water molecules or peptide backbone atoms. This error was highly sensitive to changes in electrostatic parameters. Smaller errors affecting the 13 C a and 15 N atoms were also detected. We believe these errors could be useful as metrics for comparing the force-fields and parameter sets used in MD simulation because they are directly tied to errors in atomic coordinates.

A Reassessment of the Precision of Carbonate Clumped Isotope Measurements: Implications for Calibrations and Paleoclimate Reconstructions

NASA Astrophysics Data System (ADS)

Fernandez, Alvaro; Müller, Inigo A.; Rodríguez-Sanz, Laura; van Dijk, Joep; Looser, Nathan; Bernasconi, Stefano M.

2017-12-01

Carbonate clumped isotopes offer a potentially transformational tool to interpret Earth's history, but the proxy is still limited by poor interlaboratory reproducibility. Here, we focus on the uncertainties that result from the analysis of only a few replicate measurements to understand the extent to which unconstrained errors affect calibration relationships and paleoclimate reconstructions. We find that highly precise data can be routinely obtained with multiple replicate analyses, but this is not always done in many laboratories. For instance, using published estimates of external reproducibilities we find that typical clumped isotope measurements (three replicate analyses) have margins of error at the 95% confidence level (CL) that are too large for many applications. These errors, however, can be systematically reduced with more replicate measurements. Second, using a Monte Carlo-type simulation we demonstrate that the degree of disagreement on published calibration slopes is about what we should expect considering the precision of Δ47 data, the number of samples and replicate analyses, and the temperature range covered in published calibrations. Finally, we show that the way errors are typically reported in clumped isotope data can be problematic and lead to the impression that data are more precise than warranted. We recommend that uncertainties in Δ47 data should no longer be reported as the standard error of a few replicate measurements. Instead, uncertainties should be reported as margins of error at a specified confidence level (e.g., 68% or 95% CL). These error bars are a more realistic indication of the reliability of a measurement.

The Impact of Subsampling on MODIS Level-3 Statistics of Cloud Optical Thickness and Effective Radius

NASA Technical Reports Server (NTRS)

Oreopoulos, Lazaros

2004-01-01

The MODIS Level-3 optical thickness and effective radius cloud product is a gridded l deg. x 1 deg. dataset that is derived from aggregation and subsampling at 5 km of 1 km, resolution Level-2 orbital swath data (Level-2 granules). This study examines the impact of the 5 km subsampling on the mean, standard deviation and inhomogeneity parameter statistics of optical thickness and effective radius. The methodology is simple and consists of estimating mean errors for a large collection of Terra and Aqua Level-2 granules by taking the difference of the statistics at the original and subsampled resolutions. It is shown that the Level-3 sampling does not affect the various quantities investigated to the same degree, with second order moments suffering greater subsampling errors, as expected. Mean errors drop dramatically when averages over a sufficient number of regions (e.g., monthly and/or latitudinal averages) are taken, pointing to a dominance of errors that are of random nature. When histograms built from subsampled data with the same binning rules as in the Level-3 dataset are used to reconstruct the quantities of interest, the mean errors do not deteriorate significantly. The results in this paper provide guidance to users of MODIS Level-3 optical thickness and effective radius cloud products on the range of errors due to subsampling they should expect and perhaps account for, in scientific work with this dataset. In general, subsampling errors should not be a serious concern when moderate temporal and/or spatial averaging is performed.

Machine learning of molecular properties: Locality and active learning

NASA Astrophysics Data System (ADS)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

On the asymptotic standard error of a class of robust estimators of ability in dichotomous item response models.

PubMed

Magis, David

2014-11-01

In item response theory, the classical estimators of ability are highly sensitive to response disturbances and can return strongly biased estimates of the true underlying ability level. Robust methods were introduced to lessen the impact of such aberrant responses on the estimation process. The computation of asymptotic (i.e., large-sample) standard errors (ASE) for these robust estimators, however, has not yet been fully considered. This paper focuses on a broad class of robust ability estimators, defined by an appropriate selection of the weight function and the residual measure, for which the ASE is derived from the theory of estimating equations. The maximum likelihood (ML) and the robust estimators, together with their estimated ASEs, are then compared in a simulation study by generating random guessing disturbances. It is concluded that both the estimators and their ASE perform similarly in the absence of random guessing, while the robust estimator and its estimated ASE are less biased and outperform their ML counterparts in the presence of random guessing with large impact on the item response process. © 2013 The British Psychological Society.

A comparison of earthquake backprojection imaging methods for dense local arrays

NASA Astrophysics Data System (ADS)

Beskardes, G. D.; Hole, J. A.; Wang, K.; Michaelides, M.; Wu, Q.; Chapman, M. C.; Davenport, K. K.; Brown, L. D.; Quiros, D. A.

2018-03-01

Backprojection imaging has recently become a practical method for local earthquake detection and location due to the deployment of densely sampled, continuously recorded, local seismograph arrays. While backprojection sometimes utilizes the full seismic waveform, the waveforms are often pre-processed and simplified to overcome imaging challenges. Real data issues include aliased station spacing, inadequate array aperture, inaccurate velocity model, low signal-to-noise ratio, large noise bursts and varying waveform polarity. We compare the performance of backprojection with four previously used data pre-processing methods: raw waveform, envelope, short-term averaging/long-term averaging and kurtosis. Our primary goal is to detect and locate events smaller than noise by stacking prior to detection to improve the signal-to-noise ratio. The objective is to identify an optimized strategy for automated imaging that is robust in the presence of real-data issues, has the lowest signal-to-noise thresholds for detection and for location, has the best spatial resolution of the source images, preserves magnitude, and considers computational cost. Imaging method performance is assessed using a real aftershock data set recorded by the dense AIDA array following the 2011 Virginia earthquake. Our comparisons show that raw-waveform backprojection provides the best spatial resolution, preserves magnitude and boosts signal to detect events smaller than noise, but is most sensitive to velocity error, polarity error and noise bursts. On the other hand, the other methods avoid polarity error and reduce sensitivity to velocity error, but sacrifice spatial resolution and cannot effectively reduce noise by stacking. Of these, only kurtosis is insensitive to large noise bursts while being as efficient as the raw-waveform method to lower the detection threshold; however, it does not preserve the magnitude information. For automatic detection and location of events in a large data set, we therefore recommend backprojecting kurtosis waveforms, followed by a second pass on the detected events using noise-filtered raw waveforms to achieve the best of all criteria.

Research on automatic Hartmann test of membrane mirror

NASA Astrophysics Data System (ADS)

Zhong, Xing; Jin, Guang; Liu, Chunyu; Zhang, Peng

2010-10-01

Electrostatic membrane mirror is ultra-lightweight and easy to acquire a large diameter comparing with traditional optical elements, so its development and usage is the trend of future large mirrors. In order to research the control method of the static stretching membrane mirror, the surface configuration must be tested. However, membrane mirror's shape is always changed by variable voltages on the electrodes, and the optical properties of membrane materials using in our experiment are poor, so it is difficult to test membrane mirror by interferometer and null compensator method. To solve this problem, an automatic optical test procedure for membrane mirror is designed based on Hartmann screen method. The optical path includes point light source, CCD camera, splitter and diffuse transmittance screen. The spots' positions on the diffuse transmittance screen are pictured by CCD camera connected with computer, and image segmentation and centroid solving is auto processed. The CCD camera's lens distortion is measured, and fixing coefficients are given to eliminate the spots' positions recording error caused by lens distortion. To process the low sampling Hartmann test results, Zernike polynomial fitting method is applied to smooth the wave front. So low frequency error of the membrane mirror can be measured then. Errors affecting the test accuracy are also analyzed in this paper. The method proposed in this paper provides a reference for surface shape detection in membrane mirror research.

Some practical problems in implementing randomization.

PubMed

Downs, Matt; Tucker, Kathryn; Christ-Schmidt, Heidi; Wittes, Janet

2010-06-01

While often theoretically simple, implementing randomization to treatment in a masked, but confirmable, fashion can prove difficult in practice. At least three categories of problems occur in randomization: (1) bad judgment in the choice of method, (2) design and programming errors in implementing the method, and (3) human error during the conduct of the trial. This article focuses on these latter two types of errors, dealing operationally with what can go wrong after trial designers have selected the allocation method. We offer several case studies and corresponding recommendations for lessening the frequency of problems in allocating treatment or for mitigating the consequences of errors. Recommendations include: (1) reviewing the randomization schedule before starting a trial, (2) being especially cautious of systems that use on-demand random number generators, (3) drafting unambiguous randomization specifications, (4) performing thorough testing before entering a randomization system into production, (5) maintaining a dataset that captures the values investigators used to randomize participants, thereby allowing the process of treatment allocation to be reproduced and verified, (6) resisting the urge to correct errors that occur in individual treatment assignments, (7) preventing inadvertent unmasking to treatment assignments in kit allocations, and (8) checking a sample of study drug kits to allow detection of errors in drug packaging and labeling. Although we performed a literature search of documented randomization errors, the examples that we provide and the resultant recommendations are based largely on our own experience in industry-sponsored clinical trials. We do not know how representative our experience is or how common errors of the type we have seen occur. Our experience underscores the importance of verifying the integrity of the treatment allocation process before and during a trial. Clinical Trials 2010; 7: 235-245. http://ctj.sagepub.com.

Exploiting data representation for fault tolerance

DOE PAGES

Hoemmen, Mark Frederick; Elliott, J.; Sandia National Lab.; ...

2015-01-06

Incorrect computer hardware behavior may corrupt intermediate computations in numerical algorithms, possibly resulting in incorrect answers. Prior work models misbehaving hardware by randomly flipping bits in memory. We start by accepting this premise, and present an analytic model for the error introduced by a bit flip in an IEEE 754 floating-point number. We then relate this finding to the linear algebra concepts of normalization and matrix equilibration. In particular, we present a case study illustrating that normalizing both vector inputs of a dot product minimizes the probability of a single bit flip causing a large error in the dot product'smore » result. Moreover, the absolute error is either less than one or very large, which allows detection of large errors. Then, we apply this to the GMRES iterative solver. We count all possible errors that can be introduced through faults in arithmetic in the computationally intensive orthogonalization phase of GMRES, and show that when the matrix is equilibrated, the absolute error is bounded above by one.« less

45 CFR 98.102 - Content of Error Rate Reports.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Funds and State Matching and Maintenance-of-Effort (MOE Funds): (1) Percentage of cases with an error... cases in the sample with an error compared to the total number of cases in the sample; (2) Percentage of cases with an improper payment (both over and under payments), expressed as the total number of cases in...

Detecting genotyping errors and describing black bear movement in northern Idaho

Treesearch

Michael K. Schwartz; Samuel A. Cushman; Kevin S. McKelvey; Jim Hayden; Cory Engkjer

2006-01-01

Non-invasive genetic sampling has become a favored tool to enumerate wildlife. Genetic errors, caused by poor quality samples, can lead to substantial biases in numerical estimates of individuals. We demonstrate how the computer program DROPOUT can detect amplification errors (false alleles and allelic dropout) in a black bear (Ursus americanus) dataset collected in...

A Linguistic Analysis of Errors in the Compositions of Arba Minch University Students

ERIC Educational Resources Information Center

Tizazu, Yoseph

2014-01-01

This study reports the dominant linguistic errors that occur in the written productions of Arba Minch University (hereafter AMU) students. A sample of paragraphs was collected for two years from students ranging from freshmen to graduating level. The sampled compositions were then coded, described, and explained using error analysis method. Both…

A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records.

PubMed

Ouyang, Liwen; Apley, Daniel W; Mehrotra, Sanjay

2016-04-01

Electronic medical record (EMR) databases offer significant potential for developing clinical hypotheses and identifying disease risk associations by fitting statistical models that capture the relationship between a binary response variable and a set of predictor variables that represent clinical, phenotypical, and demographic data for the patient. However, EMR response data may be error prone for a variety of reasons. Performing a manual chart review to validate data accuracy is time consuming, which limits the number of chart reviews in a large database. The authors' objective is to develop a new design-of-experiments-based systematic chart validation and review (DSCVR) approach that is more powerful than the random validation sampling used in existing approaches. The DSCVR approach judiciously and efficiently selects the cases to validate (i.e., validate whether the response values are correct for those cases) for maximum information content, based only on their predictor variable values. The final predictive model will be fit using only the validation sample, ignoring the remainder of the unvalidated and unreliable error-prone data. A Fisher information based D-optimality criterion is used, and an algorithm for optimizing it is developed. The authors' method is tested in a simulation comparison that is based on a sudden cardiac arrest case study with 23 041 patients' records. This DSCVR approach, using the Fisher information based D-optimality criterion, results in a fitted model with much better predictive performance, as measured by the receiver operating characteristic curve and the accuracy in predicting whether a patient will experience the event, than a model fitted using a random validation sample. The simulation comparisons demonstrate that this DSCVR approach can produce predictive models that are significantly better than those produced from random validation sampling, especially when the event rate is low. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Performance and precision of double digestion RAD (ddRAD) genotyping in large multiplexed datasets of marine fish species.

PubMed

Maroso, F; Hillen, J E J; Pardo, B G; Gkagkavouzis, K; Coscia, I; Hermida, M; Franch, R; Hellemans, B; Van Houdt, J; Simionati, B; Taggart, J B; Nielsen, E E; Maes, G; Ciavaglia, S A; Webster, L M I; Volckaert, F A M; Martinez, P; Bargelloni, L; Ogden, R

2018-06-01

The development of Genotyping-By-Sequencing (GBS) technologies enables cost-effective analysis of large numbers of Single Nucleotide Polymorphisms (SNPs), especially in "non-model" species. Nevertheless, as such technologies enter a mature phase, biases and errors inherent to GBS are becoming evident. Here, we evaluated the performance of double digest Restriction enzyme Associated DNA (ddRAD) sequencing in SNP genotyping studies including high number of samples. Datasets of sequence data were generated from three marine teleost species (>5500 samples, >2.5 × 10 12 bases in total), using a standardized protocol. A common bioinformatics pipeline based on STACKS was established, with and without the use of a reference genome. We performed analyses throughout the production and analysis of ddRAD data in order to explore (i) the loss of information due to heterogeneous raw read number across samples; (ii) the discrepancy between expected and observed tag length and coverage; (iii) the performances of reference based vs. de novo approaches; (iv) the sources of potential genotyping errors of the library preparation/bioinformatics protocol, by comparing technical replicates. Our results showed use of a reference genome and a posteriori genotype correction improved genotyping precision. Individual read coverage was a key variable for reproducibility; variance in sequencing depth between loci in the same individual was also identified as an important factor and found to correlate to tag length. A comparison of downstream analysis carried out with ddRAD vs single SNP allele specific assay genotypes provided information about the levels of genotyping imprecision that can have a significant impact on allele frequency estimations and population assignment. The results and insights presented here will help to select and improve approaches to the analysis of large datasets based on RAD-like methodologies. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Revealed and stated preference valuation and transfer: A within-sample comparison of water quality improvement values

NASA Astrophysics Data System (ADS)

Ferrini, Silvia; Schaafsma, Marije; Bateman, Ian

2014-06-01

Benefit transfer (BT) methods are becoming increasingly important for environmental policy, but the empirical findings regarding transfer validity are mixed. A novel valuation survey was designed to obtain both stated preference (SP) and revealed preference (RP) data concerning river water quality values from a large sample of households. Both dichotomous choice and payment card contingent valuation (CV) and travel cost (TC) data were collected. Resulting valuations were directly compared and used for BT analyses using both unit value and function transfer approaches. WTP estimates are found to pass the convergence validity test. BT results show that the CV data produce lower transfer errors, below 20% for both unit value and function transfer, than TC data especially when using function transfer. Further, comparison of WTP estimates suggests that in all cases, differences between methods are larger than differences between study areas. Results show that when multiple studies are available, using welfare estimates from the same area but based on a different method consistently results in larger errors than transfers across space keeping the method constant.

Ghost imaging based on Pearson correlation coefficients

NASA Astrophysics Data System (ADS)

Yu, Wen-Kai; Yao, Xu-Ri; Liu, Xue-Feng; Li, Long-Zhen; Zhai, Guang-Jie

2015-05-01

Correspondence imaging is a new modality of ghost imaging, which can retrieve a positive/negative image by simple conditional averaging of the reference frames that correspond to relatively large/small values of the total intensity measured at the bucket detector. Here we propose and experimentally demonstrate a more rigorous and general approach in which a ghost image is retrieved by calculating a Pearson correlation coefficient between the bucket detector intensity and the brightness at a given pixel of the reference frames, and at the next pixel, and so on. Furthermore, we theoretically provide a statistical interpretation of these two imaging phenomena, and explain how the error depends on the sample size and what kind of distribution the error obeys. According to our analysis, the image signal-to-noise ratio can be greatly improved and the sampling number reduced by means of our new method. Project supported by the National Key Scientific Instrument and Equipment Development Project of China (Grant No. 2013YQ030595) and the National High Technology Research and Development Program of China (Grant No. 2013AA122902).

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Generalized analog thresholding for spike acquisition at ultralow sampling rates

PubMed Central

He, Bryan D.; Wein, Alex; Varshney, Lav R.; Kusuma, Julius; Richardson, Andrew G.

2015-01-01

Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. PMID:25904712

USGS Blind Sample Project: monitoring and evaluating laboratory analytical quality

USGS Publications Warehouse

Ludtke, Amy S.; Woodworth, Mark T.

1997-01-01

The U.S. Geological Survey (USGS) collects and disseminates information about the Nation's water resources. Surface- and ground-water samples are collected and sent to USGS laboratories for chemical analyses. The laboratories identify and quantify the constituents in the water samples. Random and systematic errors occur during sample handling, chemical analysis, and data processing. Although all errors cannot be eliminated from measurements, the magnitude of their uncertainty can be estimated and tracked over time. Since 1981, the USGS has operated an independent, external, quality-assurance project called the Blind Sample Project (BSP). The purpose of the BSP is to monitor and evaluate the quality of laboratory analytical results through the use of double-blind quality-control (QC) samples. The information provided by the BSP assists the laboratories in detecting and correcting problems in the analytical procedures. The information also can aid laboratory users in estimating the extent that laboratory errors contribute to the overall errors in their environmental data.

Comparing and Combining Data across Multiple Sources via Integration of Paired-sample Data to Correct for Measurement Error

PubMed Central

Huang, Yunda; Huang, Ying; Moodie, Zoe; Li, Sue; Self, Steve

2014-01-01

Summary In biomedical research such as the development of vaccines for infectious diseases or cancer, measures from the same assay are often collected from multiple sources or laboratories. Measurement error that may vary between laboratories needs to be adjusted for when combining samples across laboratories. We incorporate such adjustment in comparing and combining independent samples from different labs via integration of external data, collected on paired samples from the same two laboratories. We propose: 1) normalization of individual level data from two laboratories to the same scale via the expectation of true measurements conditioning on the observed; 2) comparison of mean assay values between two independent samples in the Main study accounting for inter-source measurement error; and 3) sample size calculations of the paired-sample study so that hypothesis testing error rates are appropriately controlled in the Main study comparison. Because the goal is not to estimate the true underlying measurements but to combine data on the same scale, our proposed methods do not require that the true values for the errorprone measurements are known in the external data. Simulation results under a variety of scenarios demonstrate satisfactory finite sample performance of our proposed methods when measurement errors vary. We illustrate our methods using real ELISpot assay data generated by two HIV vaccine laboratories. PMID:22764070

Utilization of all Spectral Channels of IASI for the Retrieval of the Atmospheric State

NASA Astrophysics Data System (ADS)

Del Bianco, S.; Cortesi, U.; Carli, B.

2010-12-01

The retrieval of atmospheric state parameters from broadband measurements acquired by high spectral resolution sensors, such as the Infrared Atmospheric Sounding Interferometer (IASI) onboard the Meteorological Operational (MetOp) platform, generally requires to deal with a prohibitively large number of spectral elements available from a single observation (8461 samples in the case of IASI, covering the 645-2760 cm-1 range with a resolution of 0.5 cm-1 and a spectral sampling of 0.25 cm-1). Most inversion algorithms developed for both operational and scientific analysis of IASI spectra perform a reduction of the data - typically based on channel selection, super-channel clustering or Principal Component Analysis (PCA) techniques - in order to handle the high dimensionality of the problem. Accordingly, simultaneous processing of all IASI channels received relatively low attention. Here we prove the feasibility of a retrieval approach exploiting all spectral channels of IASI, to extract information on water vapor, temperature and ozone profiles. This multi-target retrieval removes the systematic errors due to interfering parameters and makes the channel selection no longer necessary. The challenging computation is made possible by the use of a coarse spectral grid for the forward model calculation and by the abatement of the associated modeling errors through the use of a variance-covariance matrix of the residuals that takes into account all the forward model errors.

Comprehensive Anti-error Study on Power Grid Dispatching Based on Regional Regulation and Integration

NASA Astrophysics Data System (ADS)

Zhang, Yunju; Chen, Zhongyi; Guo, Ming; Lin, Shunsheng; Yan, Yinyang

2018-01-01

With the large capacity of the power system, the development trend of the large unit and the high voltage, the scheduling operation is becoming more frequent and complicated, and the probability of operation error increases. This paper aims at the problem of the lack of anti-error function, single scheduling function and low working efficiency for technical support system in regional regulation and integration, the integrated construction of the error prevention of the integrated architecture of the system of dispatching anti - error of dispatching anti - error of power network based on cloud computing has been proposed. Integrated system of error prevention of Energy Management System, EMS, and Operation Management System, OMS have been constructed either. The system architecture has good scalability and adaptability, which can improve the computational efficiency, reduce the cost of system operation and maintenance, enhance the ability of regional regulation and anti-error checking with broad development prospects.

Quantifying errors without random sampling.

PubMed

Phillips, Carl V; LaPole, Luwanna M

2003-06-12

All quantifications of mortality, morbidity, and other health measures involve numerous sources of error. The routine quantification of random sampling error makes it easy to forget that other sources of error can and should be quantified. When a quantification does not involve sampling, error is almost never quantified and results are often reported in ways that dramatically overstate their precision. We argue that the precision implicit in typical reporting is problematic and sketch methods for quantifying the various sources of error, building up from simple examples that can be solved analytically to more complex cases. There are straightforward ways to partially quantify the uncertainty surrounding a parameter that is not characterized by random sampling, such as limiting reported significant figures. We present simple methods for doing such quantifications, and for incorporating them into calculations. More complicated methods become necessary when multiple sources of uncertainty must be combined. We demonstrate that Monte Carlo simulation, using available software, can estimate the uncertainty resulting from complicated calculations with many sources of uncertainty. We apply the method to the current estimate of the annual incidence of foodborne illness in the United States. Quantifying uncertainty from systematic errors is practical. Reporting this uncertainty would more honestly represent study results, help show the probability that estimated values fall within some critical range, and facilitate better targeting of further research.

Objectified quantification of uncertainties in Bayesian atmospheric inversions

NASA Astrophysics Data System (ADS)

Berchet, A.; Pison, I.; Chevallier, F.; Bousquet, P.; Bonne, J.-L.; Paris, J.-D.

2015-05-01

Classical Bayesian atmospheric inversions process atmospheric observations and prior emissions, the two being connected by an observation operator picturing mainly the atmospheric transport. These inversions rely on prescribed errors in the observations, the prior emissions and the observation operator. When data pieces are sparse, inversion results are very sensitive to the prescribed error distributions, which are not accurately known. The classical Bayesian framework experiences difficulties in quantifying the impact of mis-specified error distributions on the optimized fluxes. In order to cope with this issue, we rely on recent research results to enhance the classical Bayesian inversion framework through a marginalization on a large set of plausible errors that can be prescribed in the system. The marginalization consists in computing inversions for all possible error distributions weighted by the probability of occurrence of the error distributions. The posterior distribution of the fluxes calculated by the marginalization is not explicitly describable. As a consequence, we carry out a Monte Carlo sampling based on an approximation of the probability of occurrence of the error distributions. This approximation is deduced from the well-tested method of the maximum likelihood estimation. Thus, the marginalized inversion relies on an automatic objectified diagnosis of the error statistics, without any prior knowledge about the matrices. It robustly accounts for the uncertainties on the error distributions, contrary to what is classically done with frozen expert-knowledge error statistics. Some expert knowledge is still used in the method for the choice of an emission aggregation pattern and of a sampling protocol in order to reduce the computation cost. The relevance and the robustness of the method is tested on a case study: the inversion of methane surface fluxes at the mesoscale with virtual observations on a realistic network in Eurasia. Observing system simulation experiments are carried out with different transport patterns, flux distributions and total prior amounts of emitted methane. The method proves to consistently reproduce the known "truth" in most cases, with satisfactory tolerance intervals. Additionally, the method explicitly provides influence scores and posterior correlation matrices. An in-depth interpretation of the inversion results is then possible. The more objective quantification of the influence of the observations on the fluxes proposed here allows us to evaluate the impact of the observation network on the characterization of the surface fluxes. The explicit correlations between emission aggregates reveal the mis-separated regions, hence the typical temporal and spatial scales the inversion can analyse. These scales are consistent with the chosen aggregation patterns.

Evaluating performance of stormwater sampling approaches using a dynamic watershed model.

PubMed

Ackerman, Drew; Stein, Eric D; Ritter, Kerry J

2011-09-01

Accurate quantification of stormwater pollutant levels is essential for estimating overall contaminant discharge to receiving waters. Numerous sampling approaches exist that attempt to balance accuracy against the costs associated with the sampling method. This study employs a novel and practical approach of evaluating the accuracy of different stormwater monitoring methodologies using stormflows and constituent concentrations produced by a fully validated continuous simulation watershed model. A major advantage of using a watershed model to simulate pollutant concentrations is that a large number of storms representing a broad range of conditions can be applied in testing the various sampling approaches. Seventy-eight distinct methodologies were evaluated by "virtual samplings" of 166 simulated storms of varying size, intensity and duration, representing 14 years of storms in Ballona Creek near Los Angeles, California. The 78 methods can be grouped into four general strategies: volume-paced compositing, time-paced compositing, pollutograph sampling, and microsampling. The performances of each sampling strategy was evaluated by comparing the (1) median relative error between the virtually sampled and the true modeled event mean concentration (EMC) of each storm (accuracy), (2) median absolute deviation about the median or "MAD" of the relative error or (precision), and (3) the percentage of storms where sampling methods were within 10% of the true EMC (combined measures of accuracy and precision). Finally, costs associated with site setup, sampling, and laboratory analysis were estimated for each method. Pollutograph sampling consistently outperformed the other three methods both in terms of accuracy and precision, but was the most costly method evaluated. Time-paced sampling consistently underestimated while volume-paced sampling over estimated the storm EMCs. Microsampling performance approached that of pollutograph sampling at a substantial cost savings. The most efficient method for routine stormwater monitoring in terms of a balance between performance and cost was volume-paced microsampling, with variable sample pacing to ensure that the entirety of the storm was captured. Pollutograph sampling is recommended if the data are to be used for detailed analysis of runoff dynamics.

A Method for Calculating the Mean Orbits of Meteor Streams

NASA Astrophysics Data System (ADS)

Voloshchuk, Yu. I.; Kashcheev, B. L.

An examination of the published catalogs of orbits of meteor streams and of a large number of works devoted to the selection of streams, their analysis and interpretation, showed that elements of stream orbits are calculated, as a rule, as arithmetical (sometimes, weighed) sample means. On the basis of these means, a search for parent bodies, a study of the evolution of swarms generating these streams, an analysis of one-dimensional and multidimensional distributions of these elements, etc., are performed. We show that systematic errors in the estimates of elements of the mean orbits are present in each of the catalogs. These errors are caused by the formal averaging of orbital elements over the sample, while ignoring the fact that they represent not only correlated, but dependent quantities, with nonlinear, in most cases, interrelations between them. Numerous examples are given of such inaccuracies, in particular, the cases where the "mean orbit of the stream" recorded by ground-based techniques does not cross the Earth's orbit. We suggest the computation algorithm, in which the averaging over the sample is carried out at the initial stage of the calculation of the mean orbit, and only for the variables required for subsequent calculations. After this, the known astrometric formulas are used to sequentially calculate all other parameters of the stream, considered now as a standard orbit. Variance analysis is used to estimate the errors in orbital elements of the streams, in the case that their orbits are obtained by averaging the orbital elements of meteoroids forming the stream, without taking into account their interdependence. The results obtained in this analysis indicate the behavior of systematic errors in the elements of orbits of meteor streams. As an example, the effect of the incorrect computation method on the distribution of elements of the stream orbits close to the orbits of asteroids of the Apollo, Aten, and Amor groups (AAA asteroids) is examined.

Eigenvector method for umbrella sampling enables error analysis

PubMed Central

Thiede, Erik H.; Van Koten, Brian; Weare, Jonathan; Dinner, Aaron R.

2016-01-01

Umbrella sampling efficiently yields equilibrium averages that depend on exploring rare states of a model by biasing simulations to windows of coordinate values and then combining the resulting data with physical weighting. Here, we introduce a mathematical framework that casts the step of combining the data as an eigenproblem. The advantage to this approach is that it facilitates error analysis. We discuss how the error scales with the number of windows. Then, we derive a central limit theorem for averages that are obtained from umbrella sampling. The central limit theorem suggests an estimator of the error contributions from individual windows, and we develop a simple and computationally inexpensive procedure for implementing it. We demonstrate this estimator for simulations of the alanine dipeptide and show that it emphasizes low free energy pathways between stable states in comparison to existing approaches for assessing error contributions. Our work suggests the possibility of using the estimator and, more generally, the eigenvector method for umbrella sampling to guide adaptation of the simulation parameters to accelerate convergence. PMID:27586912

Using a Divided Bar Apparatus to Measure Thermal Conductivity of Samples of Odd Sizes and Shapes

NASA Astrophysics Data System (ADS)

Crowell, J. "; Gosnold, W. D.

2012-12-01

Standard procedure for measuring thermal conductivity using a divided bar apparatus requires a sample that has the same surface dimensions as the heat sink/source surface in the divided bar. Heat flow is assumed to be constant throughout the column and thermal conductivity (K) is determined by measuring temperatures (T) across the sample and across standard layers and using the basic relationship Ksample=(Kstandard*(ΔT1+ΔT2)/2)/(ΔTsample). Sometimes samples are not large enough or of correct proportions to match the surface of the heat sink/source, however using the equations presented here the thermal conductivity of these samples can still be measured with a divided bar. Measurements were done on the UND Geothermal Laboratories stationary divided bar apparatus (SDB). This SDB has been designed to mimic many in-situ conditions, with a temperature range of -20C to 150C and a pressure range of 0 to 10,000 psi for samples with parallel surfaces and 0 to 3000 psi for samples with non-parallel surfaces. The heat sink/source surfaces are copper disks and have a surface area of 1,772 mm2 (2.74 in2). Layers of polycarbonate 6 mm thick with the same surface area as the copper disks are located in the heat sink and in the heat source as standards. For this study, all samples were prepared from a single piece of 4 inch limestone core. Thermal conductivities were measured for each sample as it was cut successively smaller. The above equation was adjusted to include the thicknesses (Th) of the samples and the standards and the surface areas (A) of the heat sink/source and of the sample Ksample=(Kstandard*Astandard*Thsample*(ΔT1+ΔT3))/(ΔTsample*Asample*2*Thstandard). Measuring the thermal conductivity of samples of multiple sizes, shapes, and thicknesses gave consistent values for samples with surfaces as small as 50% of the heat sink/source surface, regardless of the shape of the sample. Measuring samples with surfaces smaller than 50% of the heat sink/source surface resulted in thermal conductivity values which were too high. The cause of the error with the smaller samples is being examined as is the relationship between the amount of error in the thermal conductivity and the difference in surface areas. As more measurements are made an equation to mathematically correct for the error is being developed on in case a way to physically correct the problem cannot be determined.

Asymmetric affective forecasting errors and their correlation with subjective well-being

PubMed Central

2018-01-01

Aims Social scientists have postulated that the discrepancy between achievements and expectations affects individuals' subjective well-being. Still, little has been done to qualify and quantify such a psychological effect. Our empirical analysis assesses the consequences of positive and negative affective forecasting errors—the difference between realized and expected subjective well-being—on the subsequent level of subjective well-being. Data We use longitudinal data on a representative sample of 13,431 individuals from the German Socio-Economic Panel. In our sample, 52% of individuals are females, average age is 43 years, average years of education is 11.4 and 27% of our sample lives in East Germany. Subjective well-being (measured by self-reported life satisfaction) is assessed on a 0–10 discrete scale and its sample average is equal to 6.75 points. Methods We develop a simple theoretical framework to assess the consequences of positive and negative affective forecasting errors—the difference between realized and expected subjective well-being—on the subsequent level of subjective well-being, properly accounting for the endogenous adjustment of expectations to positive and negative affective forecasting errors, and use it to derive testable predictions. Given the theoretical framework, we estimate two panel-data equations, the first depicting the association between positive and negative affective forecasting errors and the successive level of subjective well-being and the second describing the correlation between subjective well-being expectations for the future and hedonic failures and successes. Our models control for individual fixed effects and a large battery of time-varying demographic characteristics, health and socio-economic status. Results and conclusions While surpassing expectations is uncorrelated with subjective well-being, failing to match expectations is negatively associated with subsequent realizations of subjective well-being. Expectations are positively (negatively) correlated to positive (negative) forecasting errors. We speculate that in the first case the positive adjustment in expectations is strong enough to cancel out the potential positive effects on subjective well-being of beaten expectations, while in the second case it is not, and individuals persistently bear the negative emotional consequences of not achieving expectations. PMID:29513685

Error analysis and correction of lever-type stylus profilometer based on Nelder-Mead Simplex method

NASA Astrophysics Data System (ADS)

Hu, Chunbing; Chang, Suping; Li, Bo; Wang, Junwei; Zhang, Zhongyu

2017-10-01

Due to the high measurement accuracy and wide range of applications, lever-type stylus profilometry is commonly used in industrial research areas. However, the error caused by the lever structure has a great influence on the profile measurement, thus this paper analyzes the error of high-precision large-range lever-type stylus profilometry. The errors are corrected by the Nelder-Mead Simplex method, and the results are verified by the spherical surface calibration. It can be seen that this method can effectively reduce the measurement error and improve the accuracy of the stylus profilometry in large-scale measurement.

Complementary and alternative medicine contacts by persons with mental disorders in 25 countries: results from the World Mental Health Surveys.

PubMed

de Jonge, P; Wardenaar, K J; Hoenders, H R; Evans-Lacko, S; Kovess-Masfety, V; Aguilar-Gaxiola, S; Al-Hamzawi, A; Alonso, J; Andrade, L H; Benjet, C; Bromet, E J; Bruffaerts, R; Bunting, B; Caldas-de-Almeida, J M; Dinolova, R V; Florescu, S; de Girolamo, G; Gureje, O; Haro, J M; Hu, C; Huang, Y; Karam, E G; Karam, G; Lee, S; Lépine, J-P; Levinson, D; Makanjuola, V; Navarro-Mateu, F; Pennell, B-E; Posada-Villa, J; Scott, K; Tachimori, H; Williams, D; Wojtyniak, B; Kessler, R C; Thornicroft, G

2017-12-28

A substantial proportion of persons with mental disorders seek treatment from complementary and alternative medicine (CAM) professionals. However, data on how CAM contacts vary across countries, mental disorders and their severity, and health care settings is largely lacking. The aim was therefore to investigate the prevalence of contacts with CAM providers in a large cross-national sample of persons with 12-month mental disorders. In the World Mental Health Surveys, the Composite International Diagnostic Interview was administered to determine the presence of past 12 month mental disorders in 138 801 participants aged 18-100 derived from representative general population samples. Participants were recruited between 2001 and 2012. Rates of self-reported CAM contacts for each of the 28 surveys across 25 countries and 12 mental disorder groups were calculated for all persons with past 12-month mental disorders. Mental disorders were grouped into mood disorders, anxiety disorders or behavioural disorders, and further divided by severity levels. Satisfaction with conventional care was also compared with CAM contact satisfaction. An estimated 3.6% (standard error 0.2%) of persons with a past 12-month mental disorder reported a CAM contact, which was two times higher in high-income countries (4.6%; standard error 0.3%) than in low- and middle-income countries (2.3%; standard error 0.2%). CAM contacts were largely comparable for different disorder types, but particularly high in persons receiving conventional care (8.6-17.8%). CAM contacts increased with increasing mental disorder severity. Among persons receiving specialist mental health care, CAM contacts were reported by 14.0% for severe mood disorders, 16.2% for severe anxiety disorders and 22.5% for severe behavioural disorders. Satisfaction with care was comparable with respect to CAM contacts (78.3%) and conventional care (75.6%) in persons that received both. CAM contacts are common in persons with severe mental disorders, in high-income countries, and in persons receiving conventional care. Our findings support the notion of CAM as largely complementary but are in contrast to suggestions that this concerns person with only mild, transient complaints. There was no indication that persons were less satisfied by CAM visits than by receiving conventional care. We encourage health care professionals in conventional settings to openly discuss the care patients are receiving, whether conventional or not, and their reasons for doing so.

Decreasing Errors in Reading-Related Matching to Sample Using a Delayed-Sample Procedure

ERIC Educational Resources Information Center

Doughty, Adam H.; Saunders, Kathryn J.

2009-01-01

Two men with intellectual disabilities initially demonstrated intermediate accuracy in two-choice matching-to-sample (MTS) procedures. A printed-letter identity MTS procedure was used with 1 participant, and a spoken-to-printed-word MTS procedure was used with the other participant. Errors decreased substantially under a delayed-sample procedure,…

Estimations of ABL fluxes and other turbulence parameters from Doppler lidar data

NASA Technical Reports Server (NTRS)

Gal-Chen, Tzvi; Xu, Mei; Eberhard, Wynn

1989-01-01

Techniques for extraction boundary layer parameters from measurements of a short-pulse CO2 Doppler lidar are described. The measurements are those collected during the First International Satellites Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). By continuously operating the lidar for about an hour, stable statistics of the radial velocities can be extracted. Assuming that the turbulence is horizontally homogeneous, the mean wind, its standard deviations, and the momentum fluxes were estimated. Spectral analysis of the radial velocities is also performed from which, by examining the amplitude of the power spectrum at the inertial range, the kinetic energy dissipation was deduced. Finally, using the statistical form of the Navier-Stokes equations, the surface heat flux is derived as the residual balance between the vertical gradient of the third moment of the vertical velocity and the kinetic energy dissipation. Combining many measurements would normally reduce the error provided that, it is unbiased and uncorrelated. The nature of some of the algorithms however, is such that, biased and correlated errors may be generated even though the raw measurements are not. Data processing procedures were developed that eliminate bias and minimize error correlation. Once bias and error correlations are accounted for, the large sample size is shown to reduce the errors substantially. The principal features of the derived turbulence statistics for two case studied are presented.

The Power of Low Back Pain Trials: A Systematic Review of Power, Sample Size, and Reporting of Sample Size Calculations Over Time, in Trials Published Between 1980 and 2012.

PubMed

Froud, Robert; Rajendran, Dévan; Patel, Shilpa; Bright, Philip; Bjørkli, Tom; Eldridge, Sandra; Buchbinder, Rachelle; Underwood, Martin

2017-06-01

A systematic review of nonspecific low back pain trials published between 1980 and 2012. To explore what proportion of trials have been powered to detect different bands of effect size; whether there is evidence that sample size in low back pain trials has been increasing; what proportion of trial reports include a sample size calculation; and whether likelihood of reporting sample size calculations has increased. Clinical trials should have a sample size sufficient to detect a minimally important difference for a given power and type I error rate. An underpowered trial is one within which probability of type II error is too high. Meta-analyses do not mitigate underpowered trials. Reviewers independently abstracted data on sample size at point of analysis, whether a sample size calculation was reported, and year of publication. Descriptive analyses were used to explore ability to detect effect sizes, and regression analyses to explore the relationship between sample size, or reporting sample size calculations, and time. We included 383 trials. One-third were powered to detect a standardized mean difference of less than 0.5, and 5% were powered to detect less than 0.3. The average sample size was 153 people, which increased only slightly (∼4 people/yr) from 1980 to 2000, and declined slightly (∼4.5 people/yr) from 2005 to 2011 (P < 0.00005). Sample size calculations were reported in 41% of trials. The odds of reporting a sample size calculation (compared to not reporting one) increased until 2005 and then declined (Equation is included in full-text article.). Sample sizes in back pain trials and the reporting of sample size calculations may need to be increased. It may be justifiable to power a trial to detect only large effects in the case of novel interventions. 3.

Radiative flux and forcing parameterization error in aerosol-free clear skies.

PubMed

Pincus, Robert; Mlawer, Eli J; Oreopoulos, Lazaros; Ackerman, Andrew S; Baek, Sunghye; Brath, Manfred; Buehler, Stefan A; Cady-Pereira, Karen E; Cole, Jason N S; Dufresne, Jean-Louis; Kelley, Maxwell; Li, Jiangnan; Manners, James; Paynter, David J; Roehrig, Romain; Sekiguchi, Miho; Schwarzkopf, Daniel M

2015-07-16

Radiation parameterizations in GCMs are more accurate than their predecessorsErrors in estimates of 4 ×CO 2 forcing are large, especially for solar radiationErrors depend on atmospheric state, so global mean error is unknown.

Investigating error structure of shuttle radar topography mission elevation data product

NASA Astrophysics Data System (ADS)

Becek, Kazimierz

2008-08-01

An attempt was made to experimentally assess the instrumental component of error of the C-band SRTM (SRTM). This was achieved by comparing elevation data of 302 runways from airports all over the world with the shuttle radar topography mission data product (SRTM). It was found that the rms of the instrumental error is about +/-1.55 m. Modeling of the remaining SRTM error sources, including terrain relief and pixel size, shows that downsampling from 30 m to 90 m (1 to 3 arc-sec pixels) worsened SRTM vertical accuracy threefold. It is suspected that the proximity of large metallic objects is a source of large SRTM errors. The achieved error estimates allow a pixel-based accuracy assessment of the SRTM elevation data product to be constructed. Vegetation-induced errors were not considered in this work.

Evaluation of errors in quantitative determination of asbestos in rock

NASA Astrophysics Data System (ADS)

Baietto, Oliviero; Marini, Paola; Vitaliti, Martina

2016-04-01

The quantitative determination of the content of asbestos in rock matrices is a complex operation which is susceptible to important errors. The principal methodologies for the analysis are Scanning Electron Microscopy (SEM) and Phase Contrast Optical Microscopy (PCOM). Despite the PCOM resolution is inferior to that of SEM, PCOM analysis has several advantages, including more representativity of the analyzed sample, more effective recognition of chrysotile and a lower cost. The DIATI LAA internal methodology for the analysis in PCOM is based on a mild grinding of a rock sample, its subdivision in 5-6 grain size classes smaller than 2 mm and a subsequent microscopic analysis of a portion of each class. The PCOM is based on the optical properties of asbestos and of the liquids with note refractive index in which the particles in analysis are immersed. The error evaluation in the analysis of rock samples, contrary to the analysis of airborne filters, cannot be based on a statistical distribution. In fact for airborne filters a binomial distribution (Poisson), which theoretically defines the variation in the count of fibers resulting from the observation of analysis fields, chosen randomly on the filter, can be applied. The analysis in rock matrices instead cannot lean on any statistical distribution because the most important object of the analysis is the size of the of asbestiform fibers and bundles of fibers observed and the resulting relationship between the weights of the fibrous component compared to the one granular. The error evaluation generally provided by public and private institutions varies between 50 and 150 percent, but there are not, however, specific studies that discuss the origin of the error or that link it to the asbestos content. Our work aims to provide a reliable estimation of the error in relation to the applied methodologies and to the total content of asbestos, especially for the values close to the legal limits. The error assessments must be made through the repetition of the same analysis on the same sample to try to estimate the error on the representativeness of the sample and the error related to the sensitivity of the operator, in order to provide a sufficiently reliable uncertainty of the method. We used about 30 natural rock samples with different asbestos content, performing 3 analysis on each sample to obtain a trend sufficiently representative of the percentage. Furthermore we made on one chosen sample 10 repetition of the analysis to try to define more specifically the error of the methodology.

Dynamic Method for Identifying Collected Sample Mass

NASA Technical Reports Server (NTRS)

Carson, John

2008-01-01

G-Sample is designed for sample collection missions to identify the presence and quantity of sample material gathered by spacecraft equipped with end effectors. The software method uses a maximum-likelihood estimator to identify the collected sample's mass based on onboard force-sensor measurements, thruster firings, and a dynamics model of the spacecraft. This makes sample mass identification a computation rather than a process requiring additional hardware. Simulation examples of G-Sample are provided for spacecraft model configurations with a sample collection device mounted on the end of an extended boom. In the absence of thrust knowledge errors, the results indicate that G-Sample can identify the amount of collected sample mass to within 10 grams (with 95-percent confidence) by using a force sensor with a noise and quantization floor of 50 micrometers. These results hold even in the presence of realistic parametric uncertainty in actual spacecraft inertia, center-of-mass offset, and first flexibility modes. Thrust profile knowledge is shown to be a dominant sensitivity for G-Sample, entering in a nearly one-to-one relationship with the final mass estimation error. This means thrust profiles should be well characterized with onboard accelerometers prior to sample collection. An overall sample-mass estimation error budget has been developed to approximate the effect of model uncertainty, sensor noise, data rate, and thrust profile error on the expected estimate of collected sample mass.

Primer ID Validates Template Sampling Depth and Greatly Reduces the Error Rate of Next-Generation Sequencing of HIV-1 Genomic RNA Populations

PubMed Central

Zhou, Shuntai; Jones, Corbin; Mieczkowski, Piotr

2015-01-01

ABSTRACT Validating the sampling depth and reducing sequencing errors are critical for studies of viral populations using next-generation sequencing (NGS). We previously described the use of Primer ID to tag each viral RNA template with a block of degenerate nucleotides in the cDNA primer. We now show that low-abundance Primer IDs (offspring Primer IDs) are generated due to PCR/sequencing errors. These artifactual Primer IDs can be removed using a cutoff model for the number of reads required to make a template consensus sequence. We have modeled the fraction of sequences lost due to Primer ID resampling. For a typical sequencing run, less than 10% of the raw reads are lost to offspring Primer ID filtering and resampling. The remaining raw reads are used to correct for PCR resampling and sequencing errors. We also demonstrate that Primer ID reveals bias intrinsic to PCR, especially at low template input or utilization. cDNA synthesis and PCR convert ca. 20% of RNA templates into recoverable sequences, and 30-fold sequence coverage recovers most of these template sequences. We have directly measured the residual error rate to be around 1 in 10,000 nucleotides. We use this error rate and the Poisson distribution to define the cutoff to identify preexisting drug resistance mutations at low abundance in an HIV-infected subject. Collectively, these studies show that >90% of the raw sequence reads can be used to validate template sampling depth and to dramatically reduce the error rate in assessing a genetically diverse viral population using NGS. IMPORTANCE Although next-generation sequencing (NGS) has revolutionized sequencing strategies, it suffers from serious limitations in defining sequence heterogeneity in a genetically diverse population, such as HIV-1 due to PCR resampling and PCR/sequencing errors. The Primer ID approach reveals the true sampling depth and greatly reduces errors. Knowing the sampling depth allows the construction of a model of how to maximize the recovery of sequences from input templates and to reduce resampling of the Primer ID so that appropriate multiplexing can be included in the experimental design. With the defined sampling depth and measured error rate, we are able to assign cutoffs for the accurate detection of minority variants in viral populations. This approach allows the power of NGS to be realized without having to guess about sampling depth or to ignore the problem of PCR resampling, while also being able to correct most of the errors in the data set. PMID:26041299

Increased instrument intelligence--can it reduce laboratory error?

PubMed

Jekelis, Albert W

2005-01-01

Recent literature has focused on the reduction of laboratory errors and the potential impact on patient management. This study assessed the intelligent, automated preanalytical process-control abilities in newer generation analyzers as compared with older analyzers and the impact on error reduction. Three generations of immuno-chemistry analyzers were challenged with pooled human serum samples for a 3-week period. One of the three analyzers had an intelligent process of fluidics checks, including bubble detection. Bubbles can cause erroneous results due to incomplete sample aspiration. This variable was chosen because it is the most easily controlled sample defect that can be introduced. Traditionally, lab technicians have had to visually inspect each sample for the presence of bubbles. This is time consuming and introduces the possibility of human error. Instruments with bubble detection may be able to eliminate the human factor and reduce errors associated with the presence of bubbles. Specific samples were vortexed daily to introduce a visible quantity of bubbles, then immediately placed in the daily run. Errors were defined as a reported result greater than three standard deviations below the mean and associated with incomplete sample aspiration of the analyte of the individual analyzer Three standard deviations represented the target limits of proficiency testing. The results of the assays were examined for accuracy and precision. Efficiency, measured as process throughput, was also measured to associate a cost factor and potential impact of the error detection on the overall process. The analyzer performance stratified according to their level of internal process control The older analyzers without bubble detection reported 23 erred results. The newest analyzer with bubble detection reported one specimen incorrectly. The precision and accuracy of the nonvortexed specimens were excellent and acceptable for all three analyzers. No errors were found in the nonvortexed specimens. There were no significant differences in overall process time for any of the analyzers when tests were arranged in an optimal configuration. The analyzer with advanced fluidic intelligence demostrated the greatest ability to appropriately deal with an incomplete aspiration by not processing and reporting a result for the sample. This study suggests that preanalytical process-control capabilities could reduce errors. By association, it implies that similar intelligent process controls could favorably impact the error rate and, in the case of this instrument, do it without negatively impacting process throughput. Other improvements may be realized as a result of having an intelligent error-detection process including further reduction in misreported results, fewer repeats, less operator intervention, and less reagent waste.

Student Errors in Fractions and Possible Causes of These Errors

ERIC Educational Resources Information Center

Aksoy, Nuri Can; Yazlik, Derya Ozlem

2017-01-01

In this study, it was aimed to determine the errors and misunderstandings of 5th and 6th grade middle school students in fractions and operations with fractions. For this purpose, the case study model, which is a qualitative research design, was used in the research. In the study, maximum diversity sampling, which is a purposeful sampling method,…

A /31,15/ Reed-Solomon Code for large memory systems

NASA Technical Reports Server (NTRS)

Lim, R. S.

1979-01-01

This paper describes the encoding and the decoding of a (31,15) Reed-Solomon Code for multiple-burst error correction for large memory systems. The decoding procedure consists of four steps: (1) syndrome calculation, (2) error-location polynomial calculation, (3) error-location numbers calculation, and (4) error values calculation. The principal features of the design are the use of a hardware shift register for both high-speed encoding and syndrome calculation, and the use of a commercially available (31,15) decoder for decoding Steps 2, 3 and 4.

Army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the bootstrap method.

PubMed

Nangia, Shikha; Jasper, Ahren W; Miller, Thomas F; Truhlar, Donald G

2004-02-22

The most widely used algorithm for Monte Carlo sampling of electronic transitions in trajectory surface hopping (TSH) calculations is the so-called anteater algorithm, which is inefficient for sampling low-probability nonadiabatic events. We present a new sampling scheme (called the army ants algorithm) for carrying out TSH calculations that is applicable to systems with any strength of coupling. The army ants algorithm is a form of rare event sampling whose efficiency is controlled by an input parameter. By choosing a suitable value of the input parameter the army ants algorithm can be reduced to the anteater algorithm (which is efficient for strongly coupled cases), and by optimizing the parameter the army ants algorithm may be efficiently applied to systems with low-probability events. To demonstrate the efficiency of the army ants algorithm, we performed atom-diatom scattering calculations on a model system involving weakly coupled electronic states. Fully converged quantum mechanical calculations were performed, and the probabilities for nonadiabatic reaction and nonreactive deexcitation (quenching) were found to be on the order of 10(-8). For such low-probability events the anteater sampling scheme requires a large number of trajectories ( approximately 10(10)) to obtain good statistics and converged semiclassical results. In contrast by using the new army ants algorithm converged results were obtained by running 10(5) trajectories. Furthermore, the results were found to be in excellent agreement with the quantum mechanical results. Sampling errors were estimated using the bootstrap method, which is validated for use with the army ants algorithm. (c) 2004 American Institute of Physics.

Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering

USGS Publications Warehouse

Sethi, Suresh; Linden, Daniel; Wenburg, John; Lewis, Cara; Lemons, Patrick R.; Fuller, Angela K.; Hare, Matthew P.

2016-01-01

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus (Odobenus rosmarus divergens) and fishers (Pekania pennanti). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

Errors in causal inference: an organizational schema for systematic error and random error.

PubMed

Suzuki, Etsuji; Tsuda, Toshihide; Mitsuhashi, Toshiharu; Mansournia, Mohammad Ali; Yamamoto, Eiji

2016-11-01

To provide an organizational schema for systematic error and random error in estimating causal measures, aimed at clarifying the concept of errors from the perspective of causal inference. We propose to divide systematic error into structural error and analytic error. With regard to random error, our schema shows its four major sources: nondeterministic counterfactuals, sampling variability, a mechanism that generates exposure events and measurement variability. Structural error is defined from the perspective of counterfactual reasoning and divided into nonexchangeability bias (which comprises confounding bias and selection bias) and measurement bias. Directed acyclic graphs are useful to illustrate this kind of error. Nonexchangeability bias implies a lack of "exchangeability" between the selected exposed and unexposed groups. A lack of exchangeability is not a primary concern of measurement bias, justifying its separation from confounding bias and selection bias. Many forms of analytic errors result from the small-sample properties of the estimator used and vanish asymptotically. Analytic error also results from wrong (misspecified) statistical models and inappropriate statistical methods. Our organizational schema is helpful for understanding the relationship between systematic error and random error from a previously less investigated aspect, enabling us to better understand the relationship between accuracy, validity, and precision. Copyright © 2016 Elsevier Inc. All rights reserved.

GY SAMPLING THEORY AND GEOSTATISTICS: ALTERNATE MODELS OF VARIABILITY IN CONTINUOUS MEDIA

EPA Science Inventory

In the sampling theory developed by Pierre Gy, sample variability is modeled as the sum of a set of seven discrete error components. The variogram used in geostatisties provides an alternate model in which several of Gy's error components are combined in a continuous mode...

Evaluating data mining algorithms using molecular dynamics trajectories.

PubMed

Tatsis, Vasileios A; Tjortjis, Christos; Tzirakis, Panagiotis

2013-01-01

Molecular dynamics simulations provide a sample of a molecule's conformational space. Experiments on the mus time scale, resulting in large amounts of data, are nowadays routine. Data mining techniques such as classification provide a way to analyse such data. In this work, we evaluate and compare several classification algorithms using three data sets which resulted from computer simulations, of a potential enzyme mimetic biomolecule. We evaluated 65 classifiers available in the well-known data mining toolkit Weka, using 'classification' errors to assess algorithmic performance. Results suggest that: (i) 'meta' classifiers perform better than the other groups, when applied to molecular dynamics data sets; (ii) Random Forest and Rotation Forest are the best classifiers for all three data sets; and (iii) classification via clustering yields the highest classification error. Our findings are consistent with bibliographic evidence, suggesting a 'roadmap' for dealing with such data.

New and revised 14C dates for Hawaiian surface lava flows: Paleomagnetic and geomagnetic implications

USGS Publications Warehouse

Pressline, N.; Trusdell, F.A.; Gubbins, David

2009-01-01

Radiocarbon dates have been obtained for 30 charcoal samples corresponding to 27 surface lava flows from the Mauna Loa and Kilauea volcanoes on the Island of Hawaii. The submitted charcoal was a mixture of fresh and archived material. Preparation and analysis was undertaken at the NERC Radiocarbon Laboratory in Glasgow, Scotland, and the associated SUERC Accelerator Mass Spectrometry facility. The resulting dates range from 390 years B.P. to 12,910 years B.P. with corresponding error bars an order of magnitude smaller than previously obtained using the gas-counting method. The new and revised 14C data set can aid hazard and risk assessment on the island. The data presented here also have implications for geomagnetic modelling, which at present is limited by large dating errors. Copyright 2009 by the American Geophysical Union.

Matrix effect and correction by standard addition in quantitative liquid chromatographic-mass spectrometric analysis of diarrhetic shellfish poisoning toxins.

PubMed

Ito, Shinya; Tsukada, Katsuo

2002-01-11

An evaluation of the feasibility of liquid chromatography-mass spectrometry (LC-MS) with atmospheric pressure ionization was made for quantitation of four diarrhetic shellfish poisoning toxins, okadaic acid, dinophysistoxin-1, pectenotoxin-6 and yessotoxin in scallops. When LC-MS was applied to the analysis of scallop extracts, large signal suppressions were observed due to coeluting substances from the column. To compensate for these matrix signal suppressions, the standard addition method was applied. First, the sample was analyzed and then the sample involving the addition of calibration standards is analyzed. Although this method requires two LC-MS runs per analysis, effective correction of quantitative errors was found.

Glass sample characterization

NASA Technical Reports Server (NTRS)

Ahmad, Anees

1990-01-01

The development of in-house integrated optical performance modelling capability at MSFC is described. This performance model will take into account the effects of structural and thermal distortions, as well as metrology errors in optical surfaces to predict the performance of large an complex optical systems, such as Advanced X-Ray Astrophysics Facility. The necessary hardware and software were identified to implement an integrated optical performance model. A number of design, development, and testing tasks were supported to identify the debonded mirror pad, and rebuilding of the Technology Mirror Assembly. Over 300 samples of Zerodur were prepared in different sizes and shapes for acid etching, coating, and polishing experiments to characterize the subsurface damage and stresses produced by the grinding and polishing operations.

APPLICABILITY OF La-Ce SYSTEMATICS TO PLANETARY SAMPLES.

USGS Publications Warehouse

Nakamura, Noboru; Tatsumoto, Mitsunobu; Ludwig, Kenneth R.

1984-01-01

Ce isotopic compositions in several terrestrial and extraterrestrial materials were determined in order to investigate the applicability of using Ce as an isotopic tracer to geological processes. Owing to the low abundances of **1**3**8La and **1**3**8Ce in nature, the measurements of **1**3**8Ce/**1**4**0Ce ratios of natural samples have relatively large ( greater than 0. 02%) errors, and the variations in Ce-isotope ratios were barely resolved. A tenuous anticorrelation was observed between epsilon //C//e and epsilon //N//d for terrestrial basalts and granites, indicating that with some improvement in analytical techniques the Ce isotopic composition may prove useful as a tracer for geological processes.

Maximum inflation of the type 1 error rate when sample size and allocation rate are adapted in a pre-planned interim look.

PubMed

Graf, Alexandra C; Bauer, Peter

2011-06-30

We calculate the maximum type 1 error rate of the pre-planned conventional fixed sample size test for comparing the means of independent normal distributions (with common known variance) which can be yielded when sample size and allocation rate to the treatment arms can be modified in an interim analysis. Thereby it is assumed that the experimenter fully exploits knowledge of the unblinded interim estimates of the treatment effects in order to maximize the conditional type 1 error rate. The 'worst-case' strategies require knowledge of the unknown common treatment effect under the null hypothesis. Although this is a rather hypothetical scenario it may be approached in practice when using a standard control treatment for which precise estimates are available from historical data. The maximum inflation of the type 1 error rate is substantially larger than derived by Proschan and Hunsberger (Biometrics 1995; 51:1315-1324) for design modifications applying balanced samples before and after the interim analysis. Corresponding upper limits for the maximum type 1 error rate are calculated for a number of situations arising from practical considerations (e.g. restricting the maximum sample size, not allowing sample size to decrease, allowing only increase in the sample size in the experimental treatment). The application is discussed for a motivating example. Copyright © 2011 John Wiley & Sons, Ltd.

Free-decay time-domain modal identification for large space structures

NASA Technical Reports Server (NTRS)

Kim, Hyoung M.; Vanhorn, David A.; Doiron, Harold H.

1992-01-01

Concept definition studies for the Modal Identification Experiment (MIE), a proposed space flight experiment for the Space Station Freedom (SSF), have demonstrated advantages and compatibility of free-decay time-domain modal identification techniques with the on-orbit operational constraints of large space structures. Since practical experience with modal identification using actual free-decay responses of large space structures is very limited, several numerical and test data reduction studies were conducted. Major issues and solutions were addressed, including closely-spaced modes, wide frequency range of interest, data acquisition errors, sampling delay, excitation limitations, nonlinearities, and unknown disturbances during free-decay data acquisition. The data processing strategies developed in these studies were applied to numerical simulations of the MIE, test data from a deployable truss, and launch vehicle flight data. Results of these studies indicate free-decay time-domain modal identification methods can provide accurate modal parameters necessary to characterize the structural dynamics of large space structures.

[Errors in Peruvian medical journals references].

PubMed

Huamaní, Charles; Pacheco-Romero, José

2009-01-01

References are fundamental in our studies; an adequate selection is asimportant as an adequate description. To determine the number of errors in a sample of references found in Peruvian medical journals. We reviewed 515 scientific papers references selected by systematic randomized sampling and corroborated reference information with the original document or its citation in Pubmed, LILACS or SciELO-Peru. We found errors in 47,6% (245) of the references, identifying 372 types of errors; the most frequent were errors in presentation style (120), authorship (100) and title (100), mainly due to spelling mistakes (91). References error percentage was high, varied and multiple. We suggest systematic revision of references in the editorial process as well as to extend the discussion on this theme. references, periodicals, research, bibliometrics.

Estimating the probability that the sample mean is within a desired fraction of the standard deviation of the true mean.

PubMed

Schillaci, Michael A; Schillaci, Mario E

2009-02-01

The use of small sample sizes in human and primate evolutionary research is commonplace. Estimating how well small samples represent the underlying population, however, is not commonplace. Because the accuracy of determinations of taxonomy, phylogeny, and evolutionary process are dependant upon how well the study sample represents the population of interest, characterizing the uncertainty, or potential error, associated with analyses of small sample sizes is essential. We present a method for estimating the probability that the sample mean is within a desired fraction of the standard deviation of the true mean using small (n<10) or very small (n < or = 5) sample sizes. This method can be used by researchers to determine post hoc the probability that their sample is a meaningful approximation of the population parameter. We tested the method using a large craniometric data set commonly used by researchers in the field. Given our results, we suggest that sample estimates of the population mean can be reasonable and meaningful even when based on small, and perhaps even very small, sample sizes.

Valid statistical inference methods for a case-control study with missing data.

PubMed

Tian, Guo-Liang; Zhang, Chi; Jiang, Xuejun

2018-04-01

The main objective of this paper is to derive the valid sampling distribution of the observed counts in a case-control study with missing data under the assumption of missing at random by employing the conditional sampling method and the mechanism augmentation method. The proposed sampling distribution, called the case-control sampling distribution, can be used to calculate the standard errors of the maximum likelihood estimates of parameters via the Fisher information matrix and to generate independent samples for constructing small-sample bootstrap confidence intervals. Theoretical comparisons of the new case-control sampling distribution with two existing sampling distributions exhibit a large difference. Simulations are conducted to investigate the influence of the three different sampling distributions on statistical inferences. One finding is that the conclusion by the Wald test for testing independency under the two existing sampling distributions could be completely different (even contradictory) from the Wald test for testing the equality of the success probabilities in control/case groups under the proposed distribution. A real cervical cancer data set is used to illustrate the proposed statistical methods.

Influence of video compression on the measurement error of the television system

NASA Astrophysics Data System (ADS)

Sotnik, A. V.; Yarishev, S. N.; Korotaev, V. V.

2015-05-01

Video data require a very large memory capacity. Optimal ratio quality / volume video encoding method is one of the most actual problem due to the urgent need to transfer large amounts of video over various networks. The technology of digital TV signal compression reduces the amount of data used for video stream representation. Video compression allows effective reduce the stream required for transmission and storage. It is important to take into account the uncertainties caused by compression of the video signal in the case of television measuring systems using. There are a lot digital compression methods. The aim of proposed work is research of video compression influence on the measurement error in television systems. Measurement error of the object parameter is the main characteristic of television measuring systems. Accuracy characterizes the difference between the measured value abd the actual parameter value. Errors caused by the optical system can be selected as a source of error in the television systems measurements. Method of the received video signal processing is also a source of error. Presence of error leads to large distortions in case of compression with constant data stream rate. Presence of errors increases the amount of data required to transmit or record an image frame in case of constant quality. The purpose of the intra-coding is reducing of the spatial redundancy within a frame (or field) of television image. This redundancy caused by the strong correlation between the elements of the image. It is possible to convert an array of image samples into a matrix of coefficients that are not correlated with each other, if one can find corresponding orthogonal transformation. It is possible to apply entropy coding to these uncorrelated coefficients and achieve a reduction in the digital stream. One can select such transformation that most of the matrix coefficients will be almost zero for typical images . Excluding these zero coefficients also possible reducing of the digital stream. Discrete cosine transformation is most widely used among possible orthogonal transformation. Errors of television measuring systems and data compression protocols analyzed In this paper. The main characteristics of measuring systems and detected sources of their error detected. The most effective methods of video compression are determined. The influence of video compression error on television measuring systems was researched. Obtained results will increase the accuracy of the measuring systems. In television image quality measuring system reduces distortion identical distortion in analog systems and specific distortions resulting from the process of coding / decoding digital video signal and errors in the transmission channel. By the distortions associated with encoding / decoding signal include quantization noise, reducing resolution, mosaic effect, "mosquito" effect edging on sharp drops brightness, blur colors, false patterns, the effect of "dirty window" and other defects. The size of video compression algorithms used in television measuring systems based on the image encoding with intra- and inter prediction individual fragments. The process of encoding / decoding image is non-linear in space and in time, because the quality of the playback of a movie at the reception depends on the pre- and post-history of a random, from the preceding and succeeding tracks, which can lead to distortion of the inadequacy of the sub-picture and a corresponding measuring signal.

Radiation-Hardened Solid-State Drive

NASA Technical Reports Server (NTRS)

Sheldon, Douglas J.

2010-01-01

A method is provided for a radiationhardened (rad-hard) solid-state drive for space mission memory applications by combining rad-hard and commercial off-the-shelf (COTS) non-volatile memories (NVMs) into a hybrid architecture. The architecture is controlled by a rad-hard ASIC (application specific integrated circuit) or a FPGA (field programmable gate array). Specific error handling and data management protocols are developed for use in a rad-hard environment. The rad-hard memories are smaller in overall memory density, but are used to control and manage radiation-induced errors in the main, and much larger density, non-rad-hard COTS memory devices. Small amounts of rad-hard memory are used as error buffers and temporary caches for radiation-induced errors in the large COTS memories. The rad-hard ASIC/FPGA implements a variety of error-handling protocols to manage these radiation-induced errors. The large COTS memory is triplicated for protection, and CRC-based counters are calculated for sub-areas in each COTS NVM array. These counters are stored in the rad-hard non-volatile memory. Through monitoring, rewriting, regeneration, triplication, and long-term storage, radiation-induced errors in the large NV memory are managed. The rad-hard ASIC/FPGA also interfaces with the external computer buses.

Introduction to the Application of Web-Based Surveys.

ERIC Educational Resources Information Center

Timmerman, Annemarie

This paper discusses some basic assumptions and issues concerning web-based surveys. Discussion includes: assumptions regarding cost and ease of use; disadvantages of web-based surveys, concerning the inability to compensate for four common errors of survey research: coverage error, sampling error, measurement error and nonresponse error; and…

Successful application of FTA Classic Card technology and use of bacteriophage phi29 DNA polymerase for large-scale field sampling and cloning of complete maize streak virus genomes.

PubMed

Owor, Betty E; Shepherd, Dionne N; Taylor, Nigel J; Edema, Richard; Monjane, Adérito L; Thomson, Jennifer A; Martin, Darren P; Varsani, Arvind

2007-03-01

Leaf samples from 155 maize streak virus (MSV)-infected maize plants were collected from 155 farmers' fields in 23 districts in Uganda in May/June 2005 by leaf-pressing infected samples onto FTA Classic Cards. Viral DNA was successfully extracted from cards stored at room temperature for 9 months. The diversity of 127 MSV isolates was analysed by PCR-generated RFLPs. Six representative isolates having different RFLP patterns and causing either severe, moderate or mild disease symptoms, were chosen for amplification from FTA cards by bacteriophage phi29 DNA polymerase using the TempliPhi system. Full-length genomes were inserted into a cloning vector using a unique restriction enzyme site, and sequenced. The 1.3-kb PCR product amplified directly from FTA-eluted DNA and used for RFLP analysis was also cloned and sequenced. Comparison of cloned whole genome sequences with those of the original PCR products indicated that the correct virus genome had been cloned and that no errors were introduced by the phi29 polymerase. This is the first successful large-scale application of FTA card technology to the field, and illustrates the ease with which large numbers of infected samples can be collected and stored for downstream molecular applications such as diversity analysis and cloning of potentially new virus genomes.

Enhancement of the NMSU Channel Error Simulator to Provide User-Selectable Link Delays

NASA Technical Reports Server (NTRS)

Horan, Stephen; Wang, Ru-Hai

2000-01-01

This is the third in a continuing series of reports describing the development of the Space-to-Ground Link Simulator (SGLS) to be used for testing data transfers under simulated space channel conditions. The SGLS is based upon Virtual Instrument (VI) software techniques for managing the error generation, link data rate configuration, and, now, selection of the link delay value. In this report we detail the changes that needed to be made to the SGLS VI configuration to permit link delays to be added to the basic error generation and link data rate control capabilities. This was accomplished by modifying the rate-splitting VIs to include a buffer the hold the incoming data for the duration selected by the user to emulate the channel link delay. In sample tests of this configuration, the TCP/IP(sub ftp) service and the SCPS(sub fp) service were used to transmit 10-KB data files using both symmetric (both forward and return links set to 115200 bps) and unsymmetric (forward link set at 2400 bps and a return link set at 115200 bps) link configurations. Transmission times were recorded at bit error rates of 0 through 10(exp -5) to give an indication of the link performance. In these tests. we noted separate timings for the protocol setup time to initiate the file transfer and the variation in the actual file transfer time caused by channel errors. Both protocols showed similar performance to that seen earlier for the symmetric and unsymmetric channels. This time, the delays in establishing the file protocol also showed that these delays could double the transmission time and need to be accounted for in mission planning. Both protocols also showed a difficulty in transmitting large data files over large link delays. In these tests, there was no clear favorite between the TCP/IP(sub ftp) and the SCPS(sub fp). Based upon these tests, further testing is recommended to extend the results to different file transfer configurations.

Analytic Perturbation Method for Estimating Ground Flash Fraction from Satellite Lightning Observations

NASA Technical Reports Server (NTRS)

Koshak, William; Solakiewicz, Richard

2013-01-01

An analytic perturbation method is introduced for estimating the lightning ground flash fraction in a set of N lightning flashes observed by a satellite lightning mapper. The value of N is large, typically in the thousands, and the observations consist of the maximum optical group area produced by each flash. The method is tested using simulated observations that are based on Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) data. National Lightning Detection NetworkTM (NLDN) data is used to determine the flash-type (ground or cloud) of the satellite-observed flashes, and provides the ground flash fraction truth for the simulation runs. It is found that the mean ground flash fraction retrieval errors are below 0.04 across the full range 0-1 under certain simulation conditions. In general, it is demonstrated that the retrieval errors depend on many factors (i.e., the number, N, of satellite observations, the magnitude of random and systematic measurement errors, and the number of samples used to form certain climate distributions employed in the model).

Flexible sequential designs for multi-arm clinical trials.

PubMed

Magirr, D; Stallard, N; Jaki, T

2014-08-30

Adaptive designs that are based on group-sequential approaches have the benefit of being efficient as stopping boundaries can be found that lead to good operating characteristics with test decisions based solely on sufficient statistics. The drawback of these so called 'pre-planned adaptive' designs is that unexpected design changes are not possible without impacting the error rates. 'Flexible adaptive designs' on the other hand can cope with a large number of contingencies at the cost of reduced efficiency. In this work, we focus on two different approaches for multi-arm multi-stage trials, which are based on group-sequential ideas, and discuss how these 'pre-planned adaptive designs' can be modified to allow for flexibility. We then show how the added flexibility can be used for treatment selection and sample size reassessment and evaluate the impact on the error rates in a simulation study. The results show that an impressive overall procedure can be found by combining a well chosen pre-planned design with an application of the conditional error principle to allow flexible treatment selection. Copyright © 2014 John Wiley & Sons, Ltd.

Performance appraisal of VAS radiometry for GOES-4, -5 and -6

NASA Technical Reports Server (NTRS)

Chesters, D.; Robinson, W. D.

1983-01-01

The first three VISSR Atmospheric Sounders (VAS) were launched on GOES-4, -5, and -6 in 1980, 1981 and 1983. Postlaunch radiometric performance is assessed for noise, biases, registration and reliability, with special attention to calibration and problems in the data processing chain. The postlaunch performance of the VAS radiometer meets its prelaunch design specifications, particularly those related to image formation and noise reduction. The best instrument is carried on GOES-5, currently operational as GOES-EAST. Single sample noise is lower than expected, especially for the small longwave and large shortwave detectors. Detector to detector offsets are correctable to within the resolution limits of the instrument. Truncation, zero point and droop errors are insignificant. Absolute calibration errors, estimated from HIRS and from radiation transfer calculations, indicate moderate, but stable biases. Relative calibration errors from scanline to scanline are noticeable, but meet sounding requirements for temporarily and spatially averaged sounding fields of view. The VAS instrument is a potentially useful radiometer for mesoscale sounding operations. Image quality is very good. Soundings derived from quality controlled data meet prelaunch requirements when calculated with noise and bias resistant algorithms.

The DiskMass Survey. II. Error Budget

NASA Astrophysics Data System (ADS)

Bershady, Matthew A.; Verheijen, Marc A. W.; Westfall, Kyle B.; Andersen, David R.; Swaters, Rob A.; Martinsson, Thomas

2010-06-01

We present a performance analysis of the DiskMass Survey. The survey uses collisionless tracers in the form of disk stars to measure the surface density of spiral disks, to provide an absolute calibration of the stellar mass-to-light ratio (Υ_{*}), and to yield robust estimates of the dark-matter halo density profile in the inner regions of galaxies. We find that a disk inclination range of 25°-35° is optimal for our measurements, consistent with our survey design to select nearly face-on galaxies. Uncertainties in disk scale heights are significant, but can be estimated from radial scale lengths to 25% now, and more precisely in the future. We detail the spectroscopic analysis used to derive line-of-sight velocity dispersions, precise at low surface-brightness, and accurate in the presence of composite stellar populations. Our methods take full advantage of large-grasp integral-field spectroscopy and an extensive library of observed stars. We show that the baryon-to-total mass fraction ({F}_bar) is not a well-defined observational quantity because it is coupled to the halo mass model. This remains true even when the disk mass is known and spatially extended rotation curves are available. In contrast, the fraction of the rotation speed supplied by the disk at 2.2 scale lengths (disk maximality) is a robust observational indicator of the baryonic disk contribution to the potential. We construct the error budget for the key quantities: dynamical disk mass surface density (Σdyn), disk stellar mass-to-light ratio (Υ^disk_{*}), and disk maximality ({F}_{*,max}^disk≡ V^disk_{*,max}/ V_c). Random and systematic errors in these quantities for individual galaxies will be ~25%, while survey precision for sample quartiles are reduced to 10%, largely devoid of systematic errors outside of distance uncertainties.

Sensitivity of feedforward neural networks to weight errors

NASA Technical Reports Server (NTRS)

Stevenson, Maryhelen; Widrow, Bernard; Winter, Rodney

1990-01-01

An analysis is made of the sensitivity of feedforward layered networks of Adaline elements (threshold logic units) to weight errors. An approximation is derived which expresses the probability of error for an output neuron of a large network (a network with many neurons per layer) as a function of the percentage change in the weights. As would be expected, the probability of error increases with the number of layers in the network and with the percentage change in the weights. The probability of error is essentially independent of the number of weights per neuron and of the number of neurons per layer, as long as these numbers are large (on the order of 100 or more).

An integrated study of earth resources in the state of California using remote sensing techniques

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A weighted stratified double sample design using hardcopy LANDSAT-1 and ground data was utilized in developmental studies for snow water content estimation. Study results gave a correlation coefficient of 0.80 between LANDSAT sample units estimates of snow water content and ground subsamples. A basin snow water content estimate allowable error was given as 1.00 percent at the 99 percent confidence level with the same budget level utilized in conventional snow surveys. Several evapotranspiration estimation models were selected for efficient application at each level of data to be sampled. An area estimation procedure for impervious surface types of differing impermeability adjacent to stream channels was developed. This technique employs a double sample of 1:125,000 color infrared hightflight transparency data with ground or large scale photography.

A Ground Truthing Method for AVIRIS Overflights Using Canopy Absorption Spectra

NASA Technical Reports Server (NTRS)

Gamon, John A.; Serrano, Lydia; Roberts, Dar A.; Ustin, Susan L.

1996-01-01

Remote sensing for ecological field studies requires ground truthing for accurate interpretation of remote imagery. However, traditional vegetation sampling methods are time consuming and hard to relate to the scale of an AVIRIS scene. The large errors associated with manual field sampling, the contrasting formats of remote and ground data, and problems with coregistration of field sites with AVIRIS pixels can lead to difficulties in interpreting AVIRIS data. As part of a larger study of fire risk in the Santa Monica Mountains of southern California, we explored a ground-based optical method of sampling vegetation using spectrometers mounted both above and below vegetation canopies. The goal was to use optical methods to provide a rapid, consistent, and objective means of "ground truthing" that could be related both to AVIRIS imagery and to conventional ground sampling (e.g., plot harvests and pigment assays).

An empirical analysis of the quantitative effect of data when fitting quadratic and cubic polynomials

NASA Technical Reports Server (NTRS)

Canavos, G. C.

1974-01-01

A study is made of the extent to which the size of the sample affects the accuracy of a quadratic or a cubic polynomial approximation of an experimentally observed quantity, and the trend with regard to improvement in the accuracy of the approximation as a function of sample size is established. The task is made possible through a simulated analysis carried out by the Monte Carlo method in which data are simulated by using several transcendental or algebraic functions as models. Contaminated data of varying amounts are fitted to either quadratic or cubic polynomials, and the behavior of the mean-squared error of the residual variance is determined as a function of sample size. Results indicate that the effect of the size of the sample is significant only for relatively small sizes and diminishes drastically for moderate and large amounts of experimental data.

Density dependence and climate effects in Rocky Mountain elk: an application of regression with instrumental variables for population time series with sampling error.

PubMed

Creel, Scott; Creel, Michael

2009-11-01

1. Sampling error in annual estimates of population size creates two widely recognized problems for the analysis of population growth. First, if sampling error is mistakenly treated as process error, one obtains inflated estimates of the variation in true population trajectories (Staples, Taper & Dennis 2004). Second, treating sampling error as process error is thought to overestimate the importance of density dependence in population growth (Viljugrein et al. 2005; Dennis et al. 2006). 2. In ecology, state-space models are used to account for sampling error when estimating the effects of density and other variables on population growth (Staples et al. 2004; Dennis et al. 2006). In econometrics, regression with instrumental variables is a well-established method that addresses the problem of correlation between regressors and the error term, but requires fewer assumptions than state-space models (Davidson & MacKinnon 1993; Cameron & Trivedi 2005). 3. We used instrumental variables to account for sampling error and fit a generalized linear model to 472 annual observations of population size for 35 Elk Management Units in Montana, from 1928 to 2004. We compared this model with state-space models fit with the likelihood function of Dennis et al. (2006). We discuss the general advantages and disadvantages of each method. Briefly, regression with instrumental variables is valid with fewer distributional assumptions, but state-space models are more efficient when their distributional assumptions are met. 4. Both methods found that population growth was negatively related to population density and winter snow accumulation. Summer rainfall and wolf (Canis lupus) presence had much weaker effects on elk (Cervus elaphus) dynamics [though limitation by wolves is strong in some elk populations with well-established wolf populations (Creel et al. 2007; Creel & Christianson 2008)]. 5. Coupled with predictions for Montana from global and regional climate models, our results predict a substantial reduction in the limiting effect of snow accumulation on Montana elk populations in the coming decades. If other limiting factors do not operate with greater force, population growth rates would increase substantially.

Probing the Galactic Potential with Next-generation Observations of Disk Stars

NASA Astrophysics Data System (ADS)

Sumi, T.; Johnston, K. V.; Tremaine, S.; Spergel, D. N.; Majewski, S. R.

2009-07-01

Our current knowledge of the rotation curve of the Milky Way is remarkably poor compared to other galaxies, limited by the combined effects of extinction and the lack of large samples of stars with good distance estimates and proper motions. Near-future surveys promise a dramatic improvement in the number and precision of astrometric, photometric, and spectroscopic measurements of stars in the Milky Way's disk. We examine the impact of such surveys on our understanding of the Galaxy by "observing" particle realizations of nonaxisymmetric disk distributions orbiting in an axisymmetric halo with appropriate errors and then attempting to recover the underlying potential using a Markov Chain Monte Carlo approach. We demonstrate that the azimuthally averaged gravitational force field in the Galactic plane—and hence, to a lesser extent, the Galactic mass distribution—can be tightly constrained over a large range of radii using a variety of types of surveys so long as the error distribution of the measurements of the parallax, proper motion, and radial velocity are well understood and the disk is surveyed globally. One advantage of our method is that the target stars can be selected nonrandomly in real or apparent-magnitude space to ensure just such a global sample without biasing the results. Assuming that we can always measure the line-of-sight velocity of a star with at least 1 km s-1 precision, we demonstrate that the force field can be determined to better than ~1% for Galactocentric radii in the range R = 4-20 kpc using either: (1) small samples (a few hundred stars) with very accurate trigonometric parallaxes and good proper-motion measurements (uncertainties δ p,tri lsim 10 μas and δμ lsim 100 μas yr-1 respectively); (2) modest samples (~1000 stars) with good indirect parallax estimates (e.g., uncertainty in photometric parallax δ p,phot~ 10%-20%) and good proper-motion measurements (δμ ~ 100 μas yr-1) or (3) large samples (~104 stars) with good indirect parallax estimates and lower accuracy proper-motion measurements (δμ~ 1 mas yr-1). We conclude that near-future surveys, like Space Interferometry Mission Lite, Global Astrometric Interferometer for Astrophysics, and VERA, will provide the first precise mapping of the gravitational force field in the region of the Galactic disk.

Reliability and measurement error of sagittal spinal motion parameters in 220 patients with chronic low back pain using a three-dimensional measurement device.

PubMed

Mieritz, Rune M; Bronfort, Gert; Jakobsen, Markus D; Aagaard, Per; Hartvigsen, Jan

2014-09-01

A basic premise for any instrument measuring spinal motion is that reliable outcomes can be obtained on a relevant sample under standardized conditions. The purpose of this study was to assess the overall reliability and measurement error of regional spinal sagittal plane motion in patients with chronic low back pain (LBP), and then to evaluate the influence of body mass index, examiner, gender, stability of pain, and pain distribution on reliability and measurement error. This study comprises a test-retest design separated by 7 to 14 days. The patient cohort consisted of 220 individuals with chronic LBP. Kinematics of the lumbar spine were sampled during standardized spinal extension-flexion testing using a 6-df instrumented spatial linkage system. Test-retest reliability and measurement error were evaluated using interclass correlation coefficients (ICC(1,1)) and Bland-Altman limits of agreement (LOAs). The overall test-retest reliability (ICC(1,1)) for various motion parameters ranged from 0.51 to 0.70, and relatively wide LOAs were observed for all parameters. Reliability measures in patient subgroups (ICC(1,1)) ranged between 0.34 and 0.77. In general, greater (ICC(1,1)) coefficients and smaller LOAs were found in subgroups with patients examined by the same examiner, patients with a stable pain level, patients with a body mass index less than below 30 kg/m(2), patients who were men, and patients in the Quebec Task Force classifications Group 1. This study shows that sagittal plane kinematic data from patients with chronic LBP may be sufficiently reliable in measurements of groups of patients. However, because of the large LOAs, this test procedure appears unusable at the individual patient level. Furthermore, reliability and measurement error varies substantially among subgroups of patients. Copyright © 2014 Elsevier Inc. All rights reserved.

Interpretations of evidence for large Pleistocene paleolakes in the Bonneville basin, western North America: COMMENT on: Bonneville basin shoreline records of large lake intervals during marine isotope stage 3 and the last glacial maximum, by Nishizawa et al. (2013)

USGS Publications Warehouse

Oviatt, Charles G.; Chan, Margorie A.; Jewell, Paul W.; Bills, Bruce G.; Madsen, David B.; Miller, David

2014-01-01

Nishizawa et al. (2013) argue in support of three large paleolakes in the Bonneville basin during Marine Oxygen Isotope Stage 3 (MIS 3). If true, that would be an important contribution to paleoclimate investigations. However, the key evidence in support of their argument consists of four radiocarbon ages that are out of stratigraphic order and near the practical and theoretical limit of the dating method. The interpretation of three large MIS 3 lakes conflicts with some of their own data, as well as with independently derived stratigraphic information from the basin. Nishizawa et al. (2013) also interpret a series of radiocarbon ages of mollusk samples as indicating previously undocumented lake transgressions a few thousand years older than basal radiocarbon ages of wood samples. We believe that these interpretations are in error, and arise largely from reliance on radiocarbon ages from carbonate material. Lake records constrained by ages of non-carbonate organic materials, along with compelling stratigraphic information from unconformities and buried soils, argue for not changing interpretations of Lake Bonneville history until more supporting information for older lakes at relatively high altitudes is found.

Testing the Accuracy of Data-driven MHD Simulations of Active Region Evolution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leake, James E.; Linton, Mark G.; Schuck, Peter W., E-mail: james.e.leake@nasa.gov

Models for the evolution of the solar coronal magnetic field are vital for understanding solar activity, yet the best measurements of the magnetic field lie at the photosphere, necessitating the development of coronal models which are “data-driven” at the photosphere. We present an investigation to determine the feasibility and accuracy of such methods. Our validation framework uses a simulation of active region (AR) formation, modeling the emergence of magnetic flux from the convection zone to the corona, as a ground-truth data set, to supply both the photospheric information and to perform the validation of the data-driven method. We focus ourmore » investigation on how the accuracy of the data-driven model depends on the temporal frequency of the driving data. The Helioseismic and Magnetic Imager on NASA’s Solar Dynamics Observatory produces full-disk vector magnetic field measurements at a 12-minute cadence. Using our framework we show that ARs that emerge over 25 hr can be modeled by the data-driving method with only ∼1% error in the free magnetic energy, assuming the photospheric information is specified every 12 minutes. However, for rapidly evolving features, under-sampling of the dynamics at this cadence leads to a strobe effect, generating large electric currents and incorrect coronal morphology and energies. We derive a sampling condition for the driving cadence based on the evolution of these small-scale features, and show that higher-cadence driving can lead to acceptable errors. Future work will investigate the source of errors associated with deriving plasma variables from the photospheric magnetograms as well as other sources of errors, such as reduced resolution, instrument bias, and noise.« less

Quantification of errors in ordinal outcome scales using shannon entropy: effect on sample size calculations.

PubMed

Mandava, Pitchaiah; Krumpelman, Chase S; Shah, Jharna N; White, Donna L; Kent, Thomas A

2013-01-01

Clinical trial outcomes often involve an ordinal scale of subjective functional assessments but the optimal way to quantify results is not clear. In stroke, the most commonly used scale, the modified Rankin Score (mRS), a range of scores ("Shift") is proposed as superior to dichotomization because of greater information transfer. The influence of known uncertainties in mRS assessment has not been quantified. We hypothesized that errors caused by uncertainties could be quantified by applying information theory. Using Shannon's model, we quantified errors of the "Shift" compared to dichotomized outcomes using published distributions of mRS uncertainties and applied this model to clinical trials. We identified 35 randomized stroke trials that met inclusion criteria. Each trial's mRS distribution was multiplied with the noise distribution from published mRS inter-rater variability to generate an error percentage for "shift" and dichotomized cut-points. For the SAINT I neuroprotectant trial, considered positive by "shift" mRS while the larger follow-up SAINT II trial was negative, we recalculated sample size required if classification uncertainty was taken into account. Considering the full mRS range, error rate was 26.1%±5.31 (Mean±SD). Error rates were lower for all dichotomizations tested using cut-points (e.g. mRS 1; 6.8%±2.89; overall p<0.001). Taking errors into account, SAINT I would have required 24% more subjects than were randomized. We show when uncertainty in assessments is considered, the lowest error rates are with dichotomization. While using the full range of mRS is conceptually appealing, a gain of information is counter-balanced by a decrease in reliability. The resultant errors need to be considered since sample size may otherwise be underestimated. In principle, we have outlined an approach to error estimation for any condition in which there are uncertainties in outcome assessment. We provide the user with programs to calculate and incorporate errors into sample size estimation.

Spectral Kernel Approach to Study Radiative Response of Climate Variables and Interannual Variability of Reflected Solar Spectrum

NASA Technical Reports Server (NTRS)

Jin, Zhonghai; Wielicki, Bruce A.; Loukachine, Constantin; Charlock, Thomas P.; Young, David; Noeel, Stefan

2011-01-01

The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.

Sampling Theory and Confidence Intervals for Effect Sizes: Using ESCI To Illustrate "Bouncing"; Confidence Intervals.

ERIC Educational Resources Information Center

Du, Yunfei

This paper discusses the impact of sampling error on the construction of confidence intervals around effect sizes. Sampling error affects the location and precision of confidence intervals. Meta-analytic resampling demonstrates that confidence intervals can haphazardly bounce around the true population parameter. Special software with graphical…

STATISTICAL DISTRIBUTIONS OF PARTICULATE MATTER AND THE ERROR ASSOCIATED WITH SAMPLING FREQUENCY. (R828678C010)

EPA Science Inventory

The distribution of particulate matter (PM) concentrations has an impact on human health effects and the setting of PM regulations. Since PM is commonly sampled on less than daily schedules, the magnitude of sampling errors needs to be determined. Daily PM data from Spokane, W...

A Homing Missile Control System to Reduce the Effects of Radome Diffraction

NASA Technical Reports Server (NTRS)

Smith, Gerald L.

1960-01-01

The problem of radome diffraction in radar-controlled homing missiles at high speeds and high altitudes is considered from the point of view of developing a control system configuration which will alleviate the deleterious effects of the diffraction. It is shown that radome diffraction is in essence a kinematic feedback of body angular velocities which causes the radar to sense large apparent line-of-sight angular velocities. The normal control system cannot distinguish between the erroneous and actual line-of-sight rates, and entirely wrong maneuvers are produced which result in large miss distances. The problem is resolved by adding to the control system a special-purpose computer which utilizes measured body angular velocity to extract from the radar output true line-of-sight information for use in steering the missile. The computer operates on the principle of sampling and storing the radar output at instants when the body angular velocity is low and using this stored information for maneuvering commands. In addition, when the angular velocity is not low the computer determines a radome diffraction compensation which is subtracted from the radar output to reduce the error in the sampled information. Analog simulation results for the proposed control system operating in a coplanar (vertical plane) attack indicate a potential decrease in miss distance to an order of magnitude below that for a conventional system. Effects of glint noise, random target maneuvers, initial heading errors, and missile maneuverability are considered in the investigation.

Impact of Educational Activities in Reducing Pre-Analytical Laboratory Errors

PubMed Central

Al-Ghaithi, Hamed; Pathare, Anil; Al-Mamari, Sahimah; Villacrucis, Rodrigo; Fawaz, Naglaa; Alkindi, Salam

2017-01-01

Objectives Pre-analytic errors during diagnostic laboratory investigations can lead to increased patient morbidity and mortality. This study aimed to ascertain the effect of educational nursing activities on the incidence of pre-analytical errors resulting in non-conforming blood samples. Methods This study was conducted between January 2008 and December 2015. All specimens received at the Haematology Laboratory of the Sultan Qaboos University Hospital, Muscat, Oman, during this period were prospectively collected and analysed. Similar data from 2007 were collected retrospectively and used as a baseline for comparison. Non-conforming samples were defined as either clotted samples, haemolysed samples, use of the wrong anticoagulant, insufficient quantities of blood collected, incorrect/lack of labelling on a sample or lack of delivery of a sample in spite of a sample request. From 2008 onwards, multiple educational training activities directed at the hospital nursing staff and nursing students primarily responsible for blood collection were implemented on a regular basis. Results After initiating corrective measures in 2008, a progressive reduction in the percentage of non-conforming samples was observed from 2009 onwards. Despite a 127.84% increase in the total number of specimens received, there was a significant reduction in non-conforming samples from 0.29% in 2007 to 0.07% in 2015, resulting in an improvement of 75.86% (P <0.050). In particular, specimen identification errors decreased by 0.056%, with a 96.55% improvement. Conclusion Targeted educational activities directed primarily towards hospital nursing staff had a positive impact on the quality of laboratory specimens by significantly reducing pre-analytical errors. PMID:29062553

Impact of Educational Activities in Reducing Pre-Analytical Laboratory Errors: A quality initiative.

PubMed

Al-Ghaithi, Hamed; Pathare, Anil; Al-Mamari, Sahimah; Villacrucis, Rodrigo; Fawaz, Naglaa; Alkindi, Salam

2017-08-01

Pre-analytic errors during diagnostic laboratory investigations can lead to increased patient morbidity and mortality. This study aimed to ascertain the effect of educational nursing activities on the incidence of pre-analytical errors resulting in non-conforming blood samples. This study was conducted between January 2008 and December 2015. All specimens received at the Haematology Laboratory of the Sultan Qaboos University Hospital, Muscat, Oman, during this period were prospectively collected and analysed. Similar data from 2007 were collected retrospectively and used as a baseline for comparison. Non-conforming samples were defined as either clotted samples, haemolysed samples, use of the wrong anticoagulant, insufficient quantities of blood collected, incorrect/lack of labelling on a sample or lack of delivery of a sample in spite of a sample request. From 2008 onwards, multiple educational training activities directed at the hospital nursing staff and nursing students primarily responsible for blood collection were implemented on a regular basis. After initiating corrective measures in 2008, a progressive reduction in the percentage of non-conforming samples was observed from 2009 onwards. Despite a 127.84% increase in the total number of specimens received, there was a significant reduction in non-conforming samples from 0.29% in 2007 to 0.07% in 2015, resulting in an improvement of 75.86% ( P <0.050). In particular, specimen identification errors decreased by 0.056%, with a 96.55% improvement. Targeted educational activities directed primarily towards hospital nursing staff had a positive impact on the quality of laboratory specimens by significantly reducing pre-analytical errors.

Sample Training Based Wildfire Segmentation by 2D Histogram θ-Division with Minimum Error

PubMed Central

Dong, Erqian; Sun, Mingui; Jia, Wenyan; Zhang, Dengyi; Yuan, Zhiyong

2013-01-01

A novel wildfire segmentation algorithm is proposed with the help of sample training based 2D histogram θ-division and minimum error. Based on minimum error principle and 2D color histogram, the θ-division methods were presented recently, but application of prior knowledge on them has not been explored. For the specific problem of wildfire segmentation, we collect sample images with manually labeled fire pixels. Then we define the probability function of error division to evaluate θ-division segmentations, and the optimal angle θ is determined by sample training. Performances in different color channels are compared, and the suitable channel is selected. To further improve the accuracy, the combination approach is presented with both θ-division and other segmentation methods such as GMM. Our approach is tested on real images, and the experiments prove its efficiency for wildfire segmentation. PMID:23878526

Using beta binomials to estimate classification uncertainty for ensemble models.

PubMed

Clark, Robert D; Liang, Wenkel; Lee, Adam C; Lawless, Michael S; Fraczkiewicz, Robert; Waldman, Marvin

2014-01-01

Quantitative structure-activity (QSAR) models have enormous potential for reducing drug discovery and development costs as well as the need for animal testing. Great strides have been made in estimating their overall reliability, but to fully realize that potential, researchers and regulators need to know how confident they can be in individual predictions. Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions. For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification - one using vote tallies and the other averaging individual network outputs - we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors. Together, these two distributions can be used to estimate the probability that a given predictive classification will be in error. Large data sets comprised of logP, Ames mutagenicity, and CYP2D6 inhibition data are used to illustrate and validate the method. The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced. Moreover, the likelihood of a given compound being prospectively misclassified as a function of the degree of consensus between networks in the ensemble could in most cases be estimated accurately from the fitted beta binomial distributions for the training pool. Confidence in an individual predictive classification by an ensemble model can be accurately assessed by examining the distributions of predictions and errors as a function of the degree of agreement among the constituent submodels. Further, ensemble uncertainty estimation can often be improved by adjusting the voting or classification threshold based on the parameters of the error distribution. Finally, the profiles for models whose predictive uncertainty estimates are not reliable provide clues to that effect without the need for comparison to an external test set.

Neural correlates of response inhibition in current and former smokers.

PubMed

Weywadt, Christina R; Kiehl, Kent A; Claus, Eric D

2017-02-15

Loss of behavioral control is a hallmark of addiction. Individual differences in basic cognitive processes such as response inhibition may be important for interrupting automatic behaviors associated with smoking and supporting prolonged abstinence. To examine how response inhibition and error monitoring processes differ as a function of smoking status, current smokers, former smokers and never smokers (N=126) completed a simple Go/No-Go task while undergoing functional magnetic resonance imaging. All groups performed similarly on the task and similarly engaged the inferior frontal gyrus and dorsal anterior cingulate cortex, regions traditionally associated with response inhibition and error monitoring, respectively. During response inhibition (i.e., Correct Rejects>Hits contrast), overall group differences emerged in the recruitment of the cerebellum, while individual group differences in error monitoring (False Alarms>Hits contrast) were seen for regions of the parietal lobe and thalamus (current smokers>former smokers), as well as regions of the bilateral cerebellum, parahippocampal gyrus and superior parietal lobe (i.e., ever smokers>never smokers). We discuss how our results replicate two previous large-sample studies that used the same Go/No-Go task and review these data in terms of network models of inhibitory and error monitoring abnormalities in addiction. Copyright © 2016 Elsevier B.V. All rights reserved.

Scalable effective-temperature reduction for quantum annealers via nested quantum annealing correction

NASA Astrophysics Data System (ADS)

Vinci, Walter; Lidar, Daniel A.

2018-02-01

Nested quantum annealing correction (NQAC) is an error-correcting scheme for quantum annealing that allows for the encoding of a logical qubit into an arbitrarily large number of physical qubits. The encoding replaces each logical qubit by a complete graph of degree C . The nesting level C represents the distance of the error-correcting code and controls the amount of protection against thermal and control errors. Theoretical mean-field analyses and empirical data obtained with a D-Wave Two quantum annealer (supporting up to 512 qubits) showed that NQAC has the potential to achieve a scalable effective-temperature reduction, Teff˜C-η , with 0 <η ≤2 . We confirm that this scaling is preserved when NQAC is tested on a D-Wave 2000Q device (supporting up to 2048 qubits). In addition, we show that NQAC can also be used in sampling problems to lower the effective-temperature of a quantum annealer. Such effective-temperature reduction is relevant for machine-learning applications. Since we demonstrate that NQAC achieves error correction via a reduction of the effective-temperature of the quantum annealing device, our results address the problem of the "temperature scaling law for quantum annealers," which requires the temperature of quantum annealers to be reduced as problems of larger sizes are attempted to be solved.

Comparisons of refractive errors between twins and singletons in Chinese school-age samples.

PubMed

Hur, Yoon-Mi; Zheng, Yingfeng; Huang, Wenyong; Ding, Xiaohu; He, Mingguang

2009-02-01

Studies have reported that refractive errors are associated with premature births. As twins have higher prevalence of prematurity than singletons, it is important to assess similarity of the prevalence of refractive errors in twins and singletons for proper interpretations and generalizations of the findings from twin studies. We compared refractive errors and diopter hours between 561 pairs of twins and 3757 singletons who are representative of school-age children (7-15 years) residing in an urban area of southern China. We found that the means and variances of the continuous measurement of spherical equivalent refractive error and diopter hours were not significantly different between twins and singletons. Although the prevalence of myopia was comparable between twins and singletons, that of hyperopia and astigmatism was slightly but significantly higher in twins than in singletons. These results are inconsistent with those of adult studies that showed no differences in refractive errors between twins and singletons. Given that the sample size of twins is relatively small and that this study is the first to demonstrate minor differences in refractive errors between twins and singletons, future replications are necessary to determine whether the slightly higher prevalence of refractive errors in twins than in singletons found in this study was due to a sampling error or to the developmental delay often observed in twins in childhood.

Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

NASA Technical Reports Server (NTRS)

Wang, Jianxin; Fisher, Brad L.; Wolff, David B.

2007-01-01

This paper describes the cubic spline based operational system for the generation of the TRMM one-minute rain rate product 2A-56 from Tipping Bucket (TB) gauge measurements. Methodological issues associated with applying the cubic spline to the TB gauge rain rate estimation are closely examined. A simulated TB gauge from a Joss-Waldvogel (JW) disdrometer is employed to evaluate effects of time scales and rain event definitions on errors of the rain rate estimation. The comparison between rain rates measured from the JW disdrometer and those estimated from the simulated TB gauge shows good overall agreement; however, the TB gauge suffers sampling problems, resulting in errors in the rain rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When one minute rain rates are averaged to 4-7 minute or longer time scales, the errors dramatically reduce. The rain event duration is very sensitive to the event definition but the event rain total is rather insensitive, provided that the events with less than 1 millimeter rain totals are excluded. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-minute TB rain rates higher and lower than 3 mm per hour, respectively. These errors decrease to 5% and 14% when TB rain rates are used at 7-minute scale. The radar reflectivity-rainrate (Ze-R) distributions drawn from large amount of 7-minute TB rain rates and radar reflectivity data are mostly insensitive to the event definition.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 1; Method and Uncertainties

NASA Technical Reports Server (NTRS)

Olson, William S.; Kummerow, Christian D.; Yang, Song; Petty, Grant W.; Tao, Wei-Kuo; Bell, Thomas L.; Braun, Scott A.; Wang, Yansen; Lang, Stephen E.; Johnson, Daniel E.

2004-01-01

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Carbon Monitoring System Flux Estimation and Attribution: Impact of ACOS-GOSAT X(CO2) Sampling on the Inference of Terrestrial Biospheric Sources and Sinks

NASA Technical Reports Server (NTRS)

Liu, Junjie; Bowman, Kevin W.; Lee, Memong; Henze, David K.; Bousserez, Nicolas; Brix, Holger; Collatz, G. James; Menemenlis, Dimitris; Ott, Lesley; Pawson, Steven;

Sample Size and Statistical Conclusions from Tests of Fit to the Rasch Model According to the Rasch Unidimensional Measurement Model (Rumm) Program in Health Outcome Measurement.

PubMed

Hagell, Peter; Westergren, Albert

Sample size is a major factor in statistical null hypothesis testing, which is the basis for many approaches to testing Rasch model fit. Few sample size recommendations for testing fit to the Rasch model concern the Rasch Unidimensional Measurement Models (RUMM) software, which features chi-square and ANOVA/F-ratio based fit statistics, including Bonferroni and algebraic sample size adjustments. This paper explores the occurrence of Type I errors with RUMM fit statistics, and the effects of algebraic sample size adjustments. Data with simulated Rasch model fitting 25-item dichotomous scales and sample sizes ranging from N = 50 to N = 2500 were analysed with and without algebraically adjusted sample sizes. Results suggest the occurrence of Type I errors with N less then or equal to 500, and that Bonferroni correction as well as downward algebraic sample size adjustment are useful to avoid such errors, whereas upward adjustment of smaller samples falsely signal misfit. Our observations suggest that sample sizes around N = 250 to N = 500 may provide a good balance for the statistical interpretation of the RUMM fit statistics studied here with respect to Type I errors and under the assumption of Rasch model fit within the examined frame of reference (i.e., about 25 item parameters well targeted to the sample).

Small Body GN and C Research Report: G-SAMPLE - An In-Flight Dynamical Method for Identifying Sample Mass [External Release Version

NASA Technical Reports Server (NTRS)

Carson, John M., III; Bayard, David S.

2006-01-01

G-SAMPLE is an in-flight dynamical method for use by sample collection missions to identify the presence and quantity of collected sample material. The G-SAMPLE method implements a maximum-likelihood estimator to identify the collected sample mass, based on onboard force sensor measurements, thruster firings, and a dynamics model of the spacecraft. With G-SAMPLE, sample mass identification becomes a computation rather than an extra hardware requirement; the added cost of cameras or other sensors for sample mass detection is avoided. Realistic simulation examples are provided for a spacecraft configuration with a sample collection device mounted on the end of an extended boom. In one representative example, a 1000 gram sample mass is estimated to within 110 grams (95% confidence) under realistic assumptions of thruster profile error, spacecraft parameter uncertainty, and sensor noise. For convenience to future mission design, an overall sample-mass estimation error budget is developed to approximate the effect of model uncertainty, sensor noise, data rate, and thrust profile error on the expected estimate of collected sample mass.

Evaluating the design of an earth radiation budget instrument with system simulations. Part 2: Minimization of instantaneous sampling errors for CERES-I

NASA Technical Reports Server (NTRS)

Stowe, Larry; Hucek, Richard; Ardanuy, Philip; Joyce, Robert

1994-01-01

Much of the new record of broadband earth radiation budget satellite measurements to be obtained during the late 1990s and early twenty-first century will come from the dual-radiometer Clouds and Earth's Radiant Energy System Instrument (CERES-I) flown aboard sun-synchronous polar orbiters. Simulation studies conducted in this work for an early afternoon satellite orbit indicate that spatial root-mean-square (rms) sampling errors of instantaneous CERES-I shortwave flux estimates will range from about 8.5 to 14.0 W/m on a 2.5 deg latitude and longitude grid resolution. Rms errors in longwave flux estimates are only about 20% as large and range from 1.5 to 3.5 W/sq m. These results are based on an optimal cross-track scanner design that includes 50% footprint overlap to eliminate gaps in the top-of-the-atmosphere coverage, and a 'smallest' footprint size to increase the ratio in the number of observations lying within to the number of observations lying on grid area boundaries. Total instantaneous measurement error also depends on the variability of anisotropic reflectance and emission patterns and on retrieval methods used to generate target area fluxes. Three retrieval procedures from both CERES-I scanners (cross-track and rotating azimuth plane) are used. (1) The baseline Earth Radiaton Budget Experiment (ERBE) procedure, which assumes that errors due to the use of mean angular dependence models (ADMs) in the radiance-to-flux inversion process nearly cancel when averaged over grid areas. (2) To estimate N, instantaneous ADMs are estimated from the multiangular, collocated observations of the two scanners. These observed models replace the mean models in computation of satellite flux estimates. (3) The scene flux approach, conducts separate target-area retrievals for each ERBE scene category and combines their results using area weighting by scene type. The ERBE retrieval performs best when the simulated radiance field departs from the ERBE mean models by less than 10%. For larger perturbations, both the scene flux and collocation methods produce less error than the ERBE retrieval. The scene flux technique is preferable, however, because it involves fewer restrictive assumptions.

TU-F-17A-05: Calculating Tumor Trajectory and Dose-Of-The-Day for Highly Mobile Tumors Using Cone-Beam CT Projections

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, B; Miften, M

2014-06-15

Purpose: Cone-beam CT (CBCT) projection images provide anatomical data in real-time over several respiratory cycles, forming a comprehensive picture of tumor movement. We developed a method using these projections to determine the trajectory and dose of highly mobile tumors during each fraction of treatment. Methods: CBCT images of a respiration phantom were acquired, where the trajectory mimicked a lung tumor with high amplitude (2.4 cm) and hysteresis. A template-matching algorithm was used to identify the location of a steel BB in each projection. A Gaussian probability density function for tumor position was calculated which best fit the observed trajectory ofmore » the BB in the imager geometry. Two methods to improve the accuracy of tumor track reconstruction were investigated: first, using respiratory phase information to refine the trajectory estimation, and second, using the Monte Carlo method to sample the estimated Gaussian tumor position distribution. 15 clinically-drawn abdominal/lung CTV volumes were used to evaluate the accuracy of the proposed methods by comparing the known and calculated BB trajectories. Results: With all methods, the mean position of the BB was determined with accuracy better than 0.1 mm, and root-mean-square (RMS) trajectory errors were lower than 5% of marker amplitude. Use of respiratory phase information decreased RMS errors by 30%, and decreased the fraction of large errors (>3 mm) by half. Mean dose to the clinical volumes was calculated with an average error of 0.1% and average absolute error of 0.3%. Dosimetric parameters D90/D95 were determined within 0.5% of maximum dose. Monte-Carlo sampling increased RMS trajectory and dosimetric errors slightly, but prevented over-estimation of dose in trajectories with high noise. Conclusions: Tumor trajectory and dose-of-the-day were accurately calculated using CBCT projections. This technique provides a widely-available method to evaluate highly-mobile tumors, and could facilitate better strategies to mitigate or compensate for motion during SBRT.« less

Accounting for sampling error when inferring population synchrony from time-series data: a Bayesian state-space modelling approach with applications.

PubMed

Santin-Janin, Hugues; Hugueny, Bernard; Aubry, Philippe; Fouchet, David; Gimenez, Olivier; Pontier, Dominique

2014-01-01

Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation. The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength. The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates.

Accounting for Sampling Error When Inferring Population Synchrony from Time-Series Data: A Bayesian State-Space Modelling Approach with Applications

PubMed Central

Santin-Janin, Hugues; Hugueny, Bernard; Aubry, Philippe; Fouchet, David; Gimenez, Olivier; Pontier, Dominique

2014-01-01

Background Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation. Methodology/Principal findings The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength. Conclusion/Significance The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates. PMID:24489839

Matching on the Disease Risk Score in Comparative Effectiveness Research of New Treatments

PubMed Central

Wyss, Richard; Ellis, Alan R.; Brookhart, M. Alan; Funk, Michele Jonsson; Girman, Cynthia J.; Simpson, Ross J.; Stürmer, Til

2016-01-01

Purpose We use simulations and an empirical example to evaluate the performance of disease risk score (DRS) matching compared with propensity score (PS) matching when controlling large numbers of covariates in settings involving newly introduced treatments. Methods We simulated a dichotomous treatment, a dichotomous outcome, and 100 baseline covariates that included both continuous and dichotomous random variables. For the empirical example, we evaluated the comparative effectiveness of dabigatran versus warfarin in preventing combined ischemic stroke and all-cause mortality. We matched treatment groups on a historically estimated DRS and again on the PS. We controlled for a high-dimensional set of covariates using 20% and 1% samples of Medicare claims data from October 2010 through December 2012. Results In simulations, matching on the DRS versus the PS generally yielded matches for more treated individuals and improved precision of the effect estimate. For the empirical example, PS and DRS matching in the 20% sample resulted in similar hazard ratios (0.88 and 0.87) and standard errors (0.04 for both methods). In the 1% sample, PS matching resulted in matches for only 92.0% of the treated population and a hazard ratio and standard error of 0.89 and 0.19, respectively, while DRS matching resulted in matches for 98.5% and a hazard ratio and standard error of 0.85 and 0.16, respectively. Conclusions When PS distributions are separated, DRS matching can improve the precision of effect estimates and allow researchers to evaluate the treatment effect in a larger proportion of the treated population. However, accurately modeling the DRS can be challenging compared with the PS. PMID:26112690

Matching on the disease risk score in comparative effectiveness research of new treatments.

PubMed

Wyss, Richard; Ellis, Alan R; Brookhart, M Alan; Jonsson Funk, Michele; Girman, Cynthia J; Simpson, Ross J; Stürmer, Til

2015-09-01

We use simulations and an empirical example to evaluate the performance of disease risk score (DRS) matching compared with propensity score (PS) matching when controlling large numbers of covariates in settings involving newly introduced treatments. We simulated a dichotomous treatment, a dichotomous outcome, and 100 baseline covariates that included both continuous and dichotomous random variables. For the empirical example, we evaluated the comparative effectiveness of dabigatran versus warfarin in preventing combined ischemic stroke and all-cause mortality. We matched treatment groups on a historically estimated DRS and again on the PS. We controlled for a high-dimensional set of covariates using 20% and 1% samples of Medicare claims data from October 2010 through December 2012. In simulations, matching on the DRS versus the PS generally yielded matches for more treated individuals and improved precision of the effect estimate. For the empirical example, PS and DRS matching in the 20% sample resulted in similar hazard ratios (0.88 and 0.87) and standard errors (0.04 for both methods). In the 1% sample, PS matching resulted in matches for only 92.0% of the treated population and a hazard ratio and standard error of 0.89 and 0.19, respectively, while DRS matching resulted in matches for 98.5% and a hazard ratio and standard error of 0.85 and 0.16, respectively. When PS distributions are separated, DRS matching can improve the precision of effect estimates and allow researchers to evaluate the treatment effect in a larger proportion of the treated population. However, accurately modeling the DRS can be challenging compared with the PS. Copyright © 2015 John Wiley & Sons, Ltd.

Model Stellar Atmospheres and Real Stellar Atmospheres and Status of the ATLAS12 Opacity Sampling Program and of New Programs for Rosseland and for Distribution Function Opacity

NASA Technical Reports Server (NTRS)

Kurucz, Robert L.

1996-01-01

I discuss errors in theory and in interpreting observations that are produced by the failure to consider resolution in space, time, and energy. I discuss convection in stellar model atmospheres and in stars. Large errors in abundances are possible such as the factor of ten error in the Li abundance for extreme Population II stars. Finally I discuss the variation of microturbulent velocity with depth, effective temperature, gravity, and abundance. These variations must be dealt with in computing models and grids and in any type of photometric calibration. I have also developed a new opacity-sampling version of my model atmosphere program called ATLAS12. It recognizes more than 1000 atomic and molecular species, each in up to 10 isotopic forms. It can treat all ions of the elements up through Zn and the first 5 ions of heavier elements up through Es. The elemental and isotopic abundances are treated as variables with depth. The fluxes predicted by ATLAS12 are not accurate in intermediate or narrow bandpass intervals because the sample size is too small. A special stripped version of the spectrum synthesis program SYNTHE is used to generate the surface flux for the converged model using the line data on CD-ROMs 1 and 15. ATLAS12 can be used to produce improved models for Am and Ap stars. It should be very useful for investigating diffusion effects in atmospheres. It can be used to model exciting stars for H II regions with abundances consistent with those of the H II region. These programs and line files will be distributed on CD-ROMs.

Evidence of Selection against Complex Mitotic-Origin Aneuploidy during Preimplantation Development

PubMed Central

McCoy, Rajiv C.; Demko, Zachary P.; Ryan, Allison; Banjevic, Milena; Hill, Matthew; Sigurjonsson, Styrmir; Rabinowitz, Matthew; Petrov, Dmitri A.

2015-01-01

Whole-chromosome imbalances affect over half of early human embryos and are the leading cause of pregnancy loss. While these errors frequently arise in oocyte meiosis, many such whole-chromosome abnormalities affecting cleavage-stage embryos are the result of chromosome missegregation occurring during the initial mitotic cell divisions. The first wave of zygotic genome activation at the 4–8 cell stage results in the arrest of a large proportion of embryos, the vast majority of which contain whole-chromosome abnormalities. Thus, the full spectrum of meiotic and mitotic errors can only be detected by sampling after the initial cell divisions, but prior to this selective filter. Here, we apply 24-chromosome preimplantation genetic screening (PGS) to 28,052 single-cell day-3 blastomere biopsies and 18,387 multi-cell day-5 trophectoderm biopsies from 6,366 in vitro fertilization (IVF) cycles. We precisely characterize the rates and patterns of whole-chromosome abnormalities at each developmental stage and distinguish errors of meiotic and mitotic origin without embryo disaggregation, based on informative chromosomal signatures. We show that mitotic errors frequently involve multiple chromosome losses that are not biased toward maternal or paternal homologs. This outcome is characteristic of spindle abnormalities and chaotic cell division detected in previous studies. In contrast to meiotic errors, our data also show that mitotic errors are not significantly associated with maternal age. PGS patients referred due to previous IVF failure had elevated rates of mitotic error, while patients referred due to recurrent pregnancy loss had elevated rates of meiotic error, controlling for maternal age. These results support the conclusion that mitotic error is the predominant mechanism contributing to pregnancy losses occurring prior to blastocyst formation. This high-resolution view of the full spectrum of whole-chromosome abnormalities affecting early embryos provides insight into the cytogenetic mechanisms underlying their formation and the consequences for human fertility. PMID:26491874

Adaptive framework to better characterize errors of apriori fluxes and observational residuals in a Bayesian setup for the urban flux inversions.

NASA Astrophysics Data System (ADS)

Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Karion, A.; Mueller, K.; Gourdji, S.; Martin, C.; Whetstone, J. R.

2017-12-01

The National Institute of Standards and Technology (NIST) supports the North-East Corridor Baltimore Washington (NEC-B/W) project and Indianapolis Flux Experiment (INFLUX) aiming to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties. These projects employ different flux estimation methods including top-down inversion approaches. The traditional Bayesian inversion method estimates emission distributions by updating prior information using atmospheric observations of Green House Gases (GHG) coupled to an atmospheric and dispersion model. The magnitude of the update is dependent upon the observed enhancement along with the assumed errors such as those associated with prior information and the atmospheric transport and dispersion model. These errors are specified within the inversion covariance matrices. The assumed structure and magnitude of the specified errors can have large impact on the emission estimates from the inversion. The main objective of this work is to build a data-adaptive model for these covariances matrices. We construct a synthetic data experiment using a Kalman Filter inversion framework (Lopez et al., 2017) employing different configurations of transport and dispersion model and an assumed prior. Unlike previous traditional Bayesian approaches, we estimate posterior emissions using regularized sample covariance matrices associated with prior errors to investigate whether the structure of the matrices help to better recover our hypothetical true emissions. To incorporate transport model error, we use ensemble of transport models combined with space-time analytical covariance to construct a covariance that accounts for errors in space and time. A Kalman Filter is then run using these covariances along with Maximum Likelihood Estimates (MLE) of the involved parameters. Preliminary results indicate that specifying sptio-temporally varying errors in the error covariances can improve the flux estimates and uncertainties. We also demonstrate that differences between the modeled and observed meteorology can be used to predict uncertainties associated with atmospheric transport and dispersion modeling which can help improve the skill of an inversion at urban scales.

Sampling Errors of SSM/I and TRMM Rainfall Averages: Comparison with Error Estimates from Surface Data and a Sample Model

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Kundu, Prasun K.; Kummerow, Christian D.; Einaudi, Franco (Technical Monitor)

2000-01-01

Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote-sensing error and, in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that Root-mean-square (RMS) random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain-gauge and radar data. This relationship was examined using satellite SSM/I data obtained over the western equatorial Pacific during TOGA COARE. RMS error inferred directly from SSM/I rainfall estimates was found to be larger than predicted from surface data, and to depend less on local rain rate than was predicted. Preliminary examination of TRMM microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be directly computed from the satellite data.

Deployment of a Secondary Concentrator to Increase the Intercept Factor of a Dish with Large Slope Errors

NASA Technical Reports Server (NTRS)

Ortabasi, U.; Gray, E.; Ogallagher, J. J.

1984-01-01

The testing of a hyperbolic trumpet non-imaging secondary concentrator with a parabolic dish having slope errors of about 10 mrad is reported. The trumpet, which has a concentration ratio of 2.1, increased the flux through a 141-mm focal aperture by 72%, with an efficiency of 96%, thus demonstrating its potential for use in tandem with cheap dishes having relatively large slope errors.

The External Quality Assessment Scheme (EQAS): Experiences of a medium sized accredited laboratory.

PubMed

Bhat, Vivek; Chavan, Preeti; Naresh, Chital; Poladia, Pratik

2015-06-15

We put forth our experiences of EQAS, analyzed the result discrepancies, reviewed the corrective actions and also put forth strategies for risk identification and prevention of potential errors in a medical laboratory. For hematology, EQAS samples - blood, peripheral and reticulocyte smears - were received quarterly every year. All the blood samples were processed on HMX hematology analyzer by Beckman-Coulter. For clinical chemistry, lyophilized samples were received and were processed on Siemens Dimension Xpand and RXL analyzers. For microbiology, EQAS samples were received quarterly every year as lyophilized strains along with smears and serological samples. In hematology no outliers were noted for reticulocyte and peripheral smear examination. Only one outlier was noted for CBC. In clinical chemistry outliers (SDI ≥ 2) were noted in 7 samples (23 parameters) out of total 36 samples (756 parameters) processed. Thirteen of these parameters were analyzed as random errors, 3 as transcriptional errors and seven instances of systemic error were noted. In microbiology, one discrepancy was noted in isolate identification and in the grading of smears for AFB by Ziehl Neelsen stain. EQAS along with IQC is a very important tool for maintaining optimal quality of services. Copyright © 2015 Elsevier B.V. All rights reserved.

Uncorrected refractive errors, presbyopia and spectacle coverage: results from a rapid assessment of refractive error survey.

PubMed

Marmamula, Srinivas; Keeffe, Jill E; Rao, Gullapalli N

2009-01-01

To investigate the prevalence of uncorrected refractive errors, presbyopia and spectacle coverage in subjects aged 15-50 years using rapid assessment methodology in the Mahabubnagar district of Andhra Pradesh, India. A population-based cross sectional study was conducted using cluster random sampling to enumerate 3,300 subjects from 55 clusters. Unaided, aided and pinhole visual acuity was assessed using a LogMAR chart at a distance of 4 meters. Near vision was assessed using N notation chart. Uncorrected refractive error was defined as presenting visual acuity worse than 6/12 but improving to at least 6/12 or better on using a pinhole. Presbyopia is defined as binocular near vision worse than N8 in subjects aged more than 35 years with binocular distance visual acuity of 6/12 or better. Of the 3,300 subjects enumerated from 55 clusters, 3,203 (97%) subjects were available for examination. Of these, 1,496 (46.7%) were females and 930 (29%) were > or = 40 years. Age and gender adjusted prevalence of uncorrected refractive errors causing visual impairment in the better eye was 2.7% (95% CI, 2.1-3.2%). Presbyopia was present in 690 (63.7%, 95% CI, 60.8-66.6%) subjects aged over 35 years. Spectacle coverage for refractive error was 29% and for presbyopia it was 19%. There is a large unmet need for refractive correction in this area in India. Rapid assessment methods are an effective means of assessing the need for services and the impact of models of care.

Implicit Monte Carlo with a linear discontinuous finite element material solution and piecewise non-constant opacity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wollaeger, Ryan T.; Wollaber, Allan B.; Urbatsch, Todd J.

2016-02-23

Here, the non-linear thermal radiative-transfer equations can be solved in various ways. One popular way is the Fleck and Cummings Implicit Monte Carlo (IMC) method. The IMC method was originally formulated with piecewise-constant material properties. For domains with a coarse spatial grid and large temperature gradients, an error known as numerical teleportation may cause artificially non-causal energy propagation and consequently an inaccurate material temperature. Source tilting is a technique to reduce teleportation error by constructing sub-spatial-cell (or sub-cell) emission profiles from which IMC particles are sampled. Several source tilting schemes exist, but some allow teleportation error to persist. We examinemore » the effect of source tilting in problems with a temperature-dependent opacity. Within each cell, the opacity is evaluated continuously from a temperature profile implied by the source tilt. For IMC, this is a new approach to modeling the opacity. We find that applying both source tilting along with a source tilt-dependent opacity can introduce another dominant error that overly inhibits thermal wavefronts. We show that we can mitigate both teleportation and under-propagation errors if we discretize the temperature equation with a linear discontinuous (LD) trial space. Our method is for opacities ~ 1/T 3, but we formulate and test a slight extension for opacities ~ 1/T 3.5, where T is temperature. We find our method avoids errors that can be incurred by IMC with continuous source tilt constructions and piecewise-constant material temperature updates.« less

Soil sail content estimation in the yellow river delta with satellite hyperspectral data

USGS Publications Warehouse

Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

2008-01-01

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

Systemic errors calibration in dynamic stitching interferometry

NASA Astrophysics Data System (ADS)

Wu, Xin; Qi, Te; Yu, Yingjie; Zhang, Linna

2016-05-01

The systemic error is the main error sauce in sub-aperture stitching calculation. In this paper, a systemic error calibration method is proposed based on pseudo shearing. This method is suitable in dynamic stitching interferometry for large optical plane. The feasibility is vibrated by some simulations and experiments.

Analysis on the dynamic error for optoelectronic scanning coordinate measurement network

NASA Astrophysics Data System (ADS)

Shi, Shendong; Yang, Linghui; Lin, Jiarui; Guo, Siyang; Ren, Yongjie

2018-01-01

Large-scale dynamic three-dimension coordinate measurement technique is eagerly demanded in equipment manufacturing. Noted for advantages of high accuracy, scale expandability and multitask parallel measurement, optoelectronic scanning measurement network has got close attention. It is widely used in large components jointing, spacecraft rendezvous and docking simulation, digital shipbuilding and automated guided vehicle navigation. At present, most research about optoelectronic scanning measurement network is focused on static measurement capacity and research about dynamic accuracy is insufficient. Limited by the measurement principle, the dynamic error is non-negligible and restricts the application. The workshop measurement and positioning system is a representative which can realize dynamic measurement function in theory. In this paper we conduct deep research on dynamic error resources and divide them two parts: phase error and synchronization error. Dynamic error model is constructed. Based on the theory above, simulation about dynamic error is carried out. Dynamic error is quantized and the rule of volatility and periodicity has been found. Dynamic error characteristics are shown in detail. The research result lays foundation for further accuracy improvement.

Risk management: correct patient and specimen identification in a surgical pathology laboratory. The experience of Infermi Hospital, Rimini, Italy.

PubMed

Fabbretti, G

2010-06-01

Because of its complex nature, surgical pathology practice is prone to error. In this report, we describe our methods for reducing error as much as possible during the pre-analytical and analytical phases. This was achieved by revising procedures, and by using computer technology and automation. Most mistakes are the result of human error in the identification and matching of patient and samples. To avoid faulty data interpretation, we employed a new comprehensive computer system that acquires all patient ID information directly from the hospital's database with a remote order entry; it also provides label and request forms via-Web where clinical information is required before sending the sample. Both patient and sample are identified directly and immediately at the site where the surgical procedures are performed. Barcode technology is used to input information at every step and automation is used for sample blocks and slides to avoid errors that occur when information is recorded or transferred by hand. Quality control checks occur at every step of the process to ensure that none of the steps are left to chance and that no phase is dependent on a single operator. The system also provides statistical analysis of errors so that new strategies can be implemented to avoid repetition. In addition, the staff receives frequent training on avoiding errors and new developments. The results have been shown promising results with a very low error rate (0.27%). None of these compromised patient health and all errors were detected before the release of the diagnosis report.

The effect of covariate mean differences on the standard error and confidence interval for the comparison of treatment means.

PubMed

Liu, Xiaofeng Steven

2011-05-01

The use of covariates is commonly believed to reduce the unexplained error variance and the standard error for the comparison of treatment means, but the reduction in the standard error is neither guaranteed nor uniform over different sample sizes. The covariate mean differences between the treatment conditions can inflate the standard error of the covariate-adjusted mean difference and can actually produce a larger standard error for the adjusted mean difference than that for the unadjusted mean difference. When the covariate observations are conceived of as randomly varying from one study to another, the covariate mean differences can be related to a Hotelling's T(2) . Using this Hotelling's T(2) statistic, one can always find a minimum sample size to achieve a high probability of reducing the standard error and confidence interval width for the adjusted mean difference. ©2010 The British Psychological Society.

Clinical vision characteristics of the congenital achromatopsias. I. Visual acuity, refractive error, and binocular status.

PubMed

Haegerstrom-Portnoy, G; Schneck, M E; Verdon, W A; Hewlett, S E

1996-07-01

Visual acuity, refractive error, and binocular status were determined in 43 autosomal recessive (AR) and 15 X-linked (XL) congenital achromats. The achromats were classified by color matching and spectral sensitivity data. Large interindividual variation in refractive error and visual acuity was present within each achromat group (complete AR, incomplete AR, and XL). However, the number of individuals with significant interocular acuity differences is very small. Most XLs are myopic; ARs show a wide range of refractive error from high myopia to high hyperopia. Acuity of the AR and XL groups was very similar. With-the-rule astigmatism of large amount is very common in achromats, particularly ARs. There is a close association between strabismus and interocular acuity differences in the ARs, with the fixating eye having better than average acuity. The large overlap of acuity and refractive error of XL and AR achromats suggests that these measures are less useful for differential diagnosis than generally indicated by the clinical literature.

Implementation and verification of a four-probe motion error measurement system for a large-scale roll lathe used in hybrid manufacturing

NASA Astrophysics Data System (ADS)

Chen, Yuan-Liu; Niu, Zengyuan; Matsuura, Daiki; Lee, Jung Chul; Shimizu, Yuki; Gao, Wei; Oh, Jeong Seok; Park, Chun Hong

2017-10-01

In this paper, a four-probe measurement system is implemented and verified for the carriage slide motion error measurement of a large-scale roll lathe used in hybrid manufacturing where a laser machining probe and a diamond cutting tool are placed on two sides of a roll workpiece for manufacturing. The motion error of the carriage slide of the roll lathe is composed of two straightness motion error components and two parallelism motion error components in the vertical and horizontal planes. Four displacement measurement probes, which are mounted on the carriage slide with respect to four opposing sides of the roll workpiece, are employed for the measurement. Firstly, based on the reversal technique, the four probes are moved by the carriage slide to scan the roll workpiece before and after a 180-degree rotation of the roll workpiece. Taking into consideration the fact that the machining accuracy of the lathe is influenced by not only the carriage slide motion error but also the gravity deformation of the large-scale roll workpiece due to its heavy weight, the vertical motion error is thus characterized relating to the deformed axis of the roll workpiece. The horizontal straightness motion error can also be synchronously obtained based on the reversal technique. In addition, based on an error separation algorithm, the vertical and horizontal parallelism motion error components are identified by scanning the rotating roll workpiece at the start and the end positions of the carriage slide, respectively. The feasibility and reliability of the proposed motion error measurement system are demonstrated by the experimental results and the measurement uncertainty analysis.

An optical/NIR survey of globular clusters in early-type galaxies. III. On the colour bimodality of globular cluster systems

NASA Astrophysics Data System (ADS)

Chies-Santos, A. L.; Larsen, S. S.; Cantiello, M.; Strader, J.; Kuntschner, H.; Wehner, E. M.; Brodie, J. P.

2012-03-01

Context. The interpretation that bimodal colour distributions of globular clusters (GCs) reflect bimodal metallicity distributions has been challenged. Non-linearities in the colour to metallicity conversions caused for example by the horizontal branch (HB) stars may be responsible for transforming a unimodal metallicity distribution into a bimodal (optical) colour distribution. Aims: We study optical/near-infrared (NIR) colour distributions of the GC systems in 14 E/S0 galaxies. Methods: We test whether the bimodal feature, generally present in optical colour distributions, remains in the optical/NIR ones. The latter colour combination is a better metallicity proxy than the former. We use KMM and GMM tests to quantify the probability that different colour distributions are better described by a bimodal, as opposed to a unimodal distribution. Results: We find that double-peaked colour distributions are more commonly seen in optical than in optical/NIR colours. For some of the galaxies where the optical (g - z) distribution is clearly bimodal, a bimodal distribution is not preferred over a unimodal one at a statistically significant level for the (g - K) and (z - K) distributions. The two most cluster-rich galaxies in our sample, NGC 4486 and NGC 4649, show some interesting differences. The (g - K) distribution of NGC 4649 is better described by a bimodal distribution, while this is true for the (g - K) distribution of NGC 4486 GCs only if restricted to a brighter sub-sample with small K-band errors (<0.05 mag). Formally, the K-band photometric errors cannot be responsible for blurring bimodal metallicity distributions to unimodal (g - K) colour distributions. However, simulations including the extra scatter in the colour-colour diagrams (not fully accounted for in the photometric errors) show that such scatter may contribute to the disappearance of bimodality in (g - K) for the full NGC 4486 sample. For the less cluster-rich galaxies results are inconclusive due to poorer statistics. Conclusions: A bimodal optical colour distribution is not necessarily an indication of an underlying bimodal metallicity distribution. Horizontal branch morphology may play an important role in shaping some of the optical GC colour distributions. However, we find tentative evidence that the (g - K) colour distributions remain bimodal in the two cluster-rich galaxies in our sample (NGC 4486 and NGC 4649) when restricted to clusters with small K-band photometric errors. This bimodality becomes less pronounced when including objects with larger errors, or for the (z - K) colour distributions. Deeper observations of large numbers of GCs will be required to reach more secure conclusions.

On Two-Stage Multiple Comparison Procedures When There Are Unequal Sample Sizes in the First Stage.

ERIC Educational Resources Information Center

Wilcox, Rand R.

1984-01-01

Two stage multiple-comparison procedures give an exact solution to problems of power and Type I errors, but require equal sample sizes in the first stage. This paper suggests a method of evaluating the experimentwise Type I error probability when the first stage has unequal sample sizes. (Author/BW)

Distribution of the two-sample t-test statistic following blinded sample size re-estimation.

PubMed

Lu, Kaifeng

2016-05-01

We consider the blinded sample size re-estimation based on the simple one-sample variance estimator at an interim analysis. We characterize the exact distribution of the standard two-sample t-test statistic at the final analysis. We describe a simulation algorithm for the evaluation of the probability of rejecting the null hypothesis at given treatment effect. We compare the blinded sample size re-estimation method with two unblinded methods with respect to the empirical type I error, the empirical power, and the empirical distribution of the standard deviation estimator and final sample size. We characterize the type I error inflation across the range of standardized non-inferiority margin for non-inferiority trials, and derive the adjusted significance level to ensure type I error control for given sample size of the internal pilot study. We show that the adjusted significance level increases as the sample size of the internal pilot study increases. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Quantifying point source emissions with atmospheric inversions and aircraft measurements: the Aliso Canyon natural gas leak as a tracer experiment

NASA Astrophysics Data System (ADS)

Gourdji, S.; Yadav, V.; Karion, A.; Mueller, K. L.; Kort, E. A.; Conley, S.; Ryerson, T. B.; Nehrkorn, T.

2017-12-01

The ability of atmospheric inverse models to detect, spatially locate and quantify emissions from large point sources in urban domains needs improvement before inversions can be used reliably as carbon monitoring tools. In this study, we use the Aliso Canyon natural gas leak from October 2015 to February 2016 (near Los Angeles, CA) as a natural tracer experiment to assess inversion quality by comparison with published estimates of leak rates calculated using a mass balance approach (Conley et al., 2016). Fourteen dedicated flights were flown in horizontal transects downwind and throughout the duration of the leak to sample CH4 mole fractions and collect meteorological information for use in the mass-balance estimates. The same CH4 observational data were then used here in geostatistical inverse models with no prior assumptions about the leak location or emission rate and flux sensitivity matrices generated using the WRF-STILT atmospheric transport model. Transport model errors were assessed by comparing WRF-STILT wind speeds, wind direction and planetary boundary layer (PBL) height to those observed on the plane; the impact of these errors in the inversions, and the optimal inversion setup for reducing their influence was also explored. WRF-STILT provides a reasonable simulation of true atmospheric conditions on most flight dates, given the complex terrain and known difficulties in simulating atmospheric transport under such conditions. Moreover, even large (>120°) errors in wind direction were found to be tolerable in terms of spatially locating the leak rate within a 5-km radius of the actual site. Errors in the WRF-STILT wind speed (>50%) and PBL height have more negative impacts on the inversions, with too high wind speeds (typically corresponding with too low PBL heights) resulting in overestimated leak rates, and vice-versa. Coarser data averaging intervals and the use of observed wind speed errors in the model-data mismatch covariance matrix are shown to help reduce the influence of transport model errors, by averaging out compensating errors and de-weighting the influence of problematic observations. This study helps to enable the integration of aircraft measurements with other tower-based data in larger inverse models that can reliably detect, locate and quantify point source emissions in urban areas.

The 6dFGS Fundamental Plane

NASA Astrophysics Data System (ADS)

Springob, Chris M.; Colless, M.; Jones, D. H.; Magoulas, C.; Mould, J. R.; Campbell, L.; Lah, P.; Lucey, J.; Merson, A.; Proctor, R.

2010-01-01

The 6dF Galaxy Survey (6dFGS) is an all southern sky galaxy survey, including 125,000 redshifts and more than 10,000 peculiar velocities, making it the largest peculiar velocity sample to date. In combination with 2MASS surface brightnesses and effective radii, 6dFGS yields the near-infrared Fundamental Plane (FP) for a large and uniform sample. We have fit the FP relation for the galaxies in the peculiar velocity sample using a maximum likelihood method which allows us to precisely account for selection effects and observational errors. We investigate the effects of varying stellar populations and environments on the FP. Finally, we discuss the implications of these results both for our understanding of the origin of the FP for early-type galaxies and bulges and for deriving unbiased distances and peculiar velocities in the local universe.

Two-sample binary phase 2 trials with low type I error and low sample size

PubMed Central

Litwin, Samuel; Basickes, Stanley; Ross, Eric A.

2017-01-01

Summary We address design of two-stage clinical trials comparing experimental and control patients. Our end-point is success or failure, however measured, with null hypothesis that the chance of success in both arms is p0 and alternative that it is p0 among controls and p1 > p0 among experimental patients. Standard rules will have the null hypothesis rejected when the number of successes in the (E)xperimental arm, E, sufficiently exceeds C, that among (C)ontrols. Here, we combine one-sample rejection decision rules, E ≥ m, with two-sample rules of the form E – C > r to achieve two-sample tests with low sample number and low type I error. We find designs with sample numbers not far from the minimum possible using standard two-sample rules, but with type I error of 5% rather than 15% or 20% associated with them, and of equal power. This level of type I error is achieved locally, near the stated null, and increases to 15% or 20% when the null is significantly higher than specified. We increase the attractiveness of these designs to patients by using 2:1 randomization. Examples of the application of this new design covering both high and low success rates under the null hypothesis are provided. PMID:28118686

[Exploration of the concept of genetic drift in genetics teaching of undergraduates].

PubMed

Wang, Chun-ming

2016-01-01

Genetic drift is one of the difficulties in teaching genetics due to its randomness and probability which could easily cause conceptual misunderstanding. The “sampling error" in its definition is often misunderstood because of the research method of “sampling", which disturbs the results and causes the random changes in allele frequency. I analyzed and compared the definitions of genetic drift in domestic and international genetic textbooks, and found that the definitions containing “sampling error" are widely adopted but are interpreted correctly in only a few textbooks. Here, the history of research on genetic drift, i.e., the contributions of Wright, Fisher and Kimura, is introduced. Moreover, I particularly describe two representative articles recently published about genetic drift teaching of undergraduates, which point out that misconceptions are inevitable for undergraduates during the studying process and also provide a preliminary solution. Combined with my own teaching practice, I suggest that the definition of genetic drift containing “sampling error" can be adopted with further interpretation, i.e., “sampling error" is random sampling among gametes when generating the next generation of alleles which is equivalent to a random sampling of all gametes participating in mating in gamete pool and has no relationship with artificial sampling in general genetics studies. This article may provide some help in genetics teaching.

Control by model error estimation

NASA Technical Reports Server (NTRS)

Likins, P. W.; Skelton, R. E.

1976-01-01

Modern control theory relies upon the fidelity of the mathematical model of the system. Truncated modes, external disturbances, and parameter errors in linear system models are corrected by augmenting to the original system of equations an 'error system' which is designed to approximate the effects of such model errors. A Chebyshev error system is developed for application to the Large Space Telescope (LST).

Performance of GPS-devices for environmental exposure assessment.

PubMed

Beekhuizen, Johan; Kromhout, Hans; Huss, Anke; Vermeulen, Roel

2013-01-01

Integration of individual time-location patterns with spatially resolved exposure maps enables a more accurate estimation of personal exposures to environmental pollutants than using estimates at fixed locations. Current global positioning system (GPS) devices can be used to track an individual's location. However, information on GPS-performance in environmental exposure assessment is largely missing. We therefore performed two studies. First, a commute-study, where the commute of 12 individuals was tracked twice, testing GPS-performance for five transport modes and two wearing modes. Second, an urban-tracking study, where one individual was tracked repeatedly through different areas, focused on the effect of building obstruction on GPS-performance. The median error from the true path for walking was 3.7 m, biking 2.9 m, train 4.8 m, bus 4.9 m, and car 3.3 m. Errors were larger in a high-rise commercial area (median error=7.1 m) compared with a low-rise residential area (median error=2.2 m). Thus, GPS-performance largely depends on the transport mode and urban built-up. Although ~85% of all errors were <10 m, almost 1% of the errors were >50 m. Modern GPS-devices are useful tools for environmental exposure assessment, but large GPS-errors might affect estimates of exposures with high spatial variability.

Uncertainty in accounting for carbon accumulation following forest harvesting

NASA Astrophysics Data System (ADS)

Lilly, P.; Yanai, R. D.; Arthur, M. A.; Bae, K.; Hamburg, S.; Levine, C. R.; Vadeboncoeur, M. A.

2014-12-01

Tree biomass and forest soils are both difficult to quantify with confidence, for different reasons. Forest biomass is estimated non-destructively using allometric equations, often from other sites; these equations are difficult to validate. Forest soils are destructively sampled, resulting in little measurement error at a point, but with large sampling error in heterogeneous soil environments, such as in soils developed on glacial till. In this study, we report C contents of biomass and soil pools in northern hardwood stands in replicate plots within replicate stands in 3 age classes following clearcut harvesting (14-19 yr, 26-29 yr, and > 100 yr) at the Bartlett Experimental Forest, USA. The rate of C accumulation in aboveground biomass was ~3 Mg/ha/yr between the young and mid-aged stands and <1 Mg/ha/yr between the mid-aged and mature stands. We propagated model uncertainty through allometric equations, and found errors ranging from 3-7%, depending on the stand. The variation in biomass among plots within stands (6-19%) was always larger than the allometric uncertainties. Soils were described by quantitative soil pits in three plots per stand, excavated by depth increment to the C horizon. Variation in soil mass among pits within stands averaged 28% (coefficient of variation); variation among stands within an age class ranged from 9-25%. Variation in carbon concentrations averaged 27%, mainly because the depth increments contained varying proportions of genetic horizons, in the upper part of the soil profile. Differences across age classes in soil C were not significant, because of the high variability. Uncertainty analysis can help direct the design of monitoring schemes to achieve the greatest confidence in C stores per unit of sampling effort. In the system we studied, more extensive sampling would be the best approach to reducing uncertainty, as natural spatial variation was higher than model or measurement uncertainties.

Effect of uncertainties on probabilistic-based design capacity of hydrosystems

NASA Astrophysics Data System (ADS)

Tung, Yeou-Koung

2018-02-01

Hydrosystems engineering designs involve analysis of hydrometric data (e.g., rainfall, floods) and use of hydrologic/hydraulic models, all of which contribute various degrees of uncertainty to the design process. Uncertainties in hydrosystem designs can be generally categorized into aleatory and epistemic types. The former arises from the natural randomness of hydrologic processes whereas the latter are due to knowledge deficiency in model formulation and model parameter specification. This study shows that the presence of epistemic uncertainties induces uncertainty in determining the design capacity. Hence, the designer needs to quantify the uncertainty features of design capacity to determine the capacity with a stipulated performance reliability under the design condition. Using detention basin design as an example, the study illustrates a methodological framework by considering aleatory uncertainty from rainfall and epistemic uncertainties from the runoff coefficient, curve number, and sampling error in design rainfall magnitude. The effects of including different items of uncertainty and performance reliability on the design detention capacity are examined. A numerical example shows that the mean value of the design capacity of the detention basin increases with the design return period and this relation is found to be practically the same regardless of the uncertainty types considered. The standard deviation associated with the design capacity, when subject to epistemic uncertainty, increases with both design frequency and items of epistemic uncertainty involved. It is found that the epistemic uncertainty due to sampling error in rainfall quantiles should not be ignored. Even with a sample size of 80 (relatively large for a hydrologic application) the inclusion of sampling error in rainfall quantiles resulted in a standard deviation about 2.5 times higher than that considering only the uncertainty of the runoff coefficient and curve number. Furthermore, the presence of epistemic uncertainties in the design would result in under-estimation of the annual failure probability of the hydrosystem and has a discounting effect on the anticipated design return period.

Novel measuring strategies in neutron interferometry

NASA Astrophysics Data System (ADS)

Bonse, Ulrich; Wroblewski, Thomas

1985-04-01

Angular misalignment of a sample in a single crystal neutron interferometer leads to systematic errors of the effective sample thickness and in this way to errors in the determination of the coherent scattering length. The misalignment can be determined and the errors can be corrected by a second measurement at a different angular sample position. Furthermore, a method has been developed which allows supervision of the wavelength during the measurements. These two techniques were tested by determining the scattering length of copper. A value of bc = 7.66(4) fm was obtained which is in excellent agreement with previous measurements.

A Third Moment Adjusted Test Statistic for Small Sample Factor Analysis.

PubMed

Lin, Johnny; Bentler, Peter M

2012-01-01

Goodness of fit testing in factor analysis is based on the assumption that the test statistic is asymptotically chi-square; but this property may not hold in small samples even when the factors and errors are normally distributed in the population. Robust methods such as Browne's asymptotically distribution-free method and Satorra Bentler's mean scaling statistic were developed under the presumption of non-normality in the factors and errors. This paper finds new application to the case where factors and errors are normally distributed in the population but the skewness of the obtained test statistic is still high due to sampling error in the observed indicators. An extension of Satorra Bentler's statistic is proposed that not only scales the mean but also adjusts the degrees of freedom based on the skewness of the obtained test statistic in order to improve its robustness under small samples. A simple simulation study shows that this third moment adjusted statistic asymptotically performs on par with previously proposed methods, and at a very small sample size offers superior Type I error rates under a properly specified model. Data from Mardia, Kent and Bibby's study of students tested for their ability in five content areas that were either open or closed book were used to illustrate the real-world performance of this statistic.

Two-step estimation in ratio-of-mediator-probability weighted causal mediation analysis.

PubMed

Bein, Edward; Deutsch, Jonah; Hong, Guanglei; Porter, Kristin E; Qin, Xu; Yang, Cheng

2018-04-15

This study investigates appropriate estimation of estimator variability in the context of causal mediation analysis that employs propensity score-based weighting. Such an analysis decomposes the total effect of a treatment on the outcome into an indirect effect transmitted through a focal mediator and a direct effect bypassing the mediator. Ratio-of-mediator-probability weighting estimates these causal effects by adjusting for the confounding impact of a large number of pretreatment covariates through propensity score-based weighting. In step 1, a propensity score model is estimated. In step 2, the causal effects of interest are estimated using weights derived from the prior step's regression coefficient estimates. Statistical inferences obtained from this 2-step estimation procedure are potentially problematic if the estimated standard errors of the causal effect estimates do not reflect the sampling uncertainty in the estimation of the weights. This study extends to ratio-of-mediator-probability weighting analysis a solution to the 2-step estimation problem by stacking the score functions from both steps. We derive the asymptotic variance-covariance matrix for the indirect effect and direct effect 2-step estimators, provide simulation results, and illustrate with an application study. Our simulation results indicate that the sampling uncertainty in the estimated weights should not be ignored. The standard error estimation using the stacking procedure offers a viable alternative to bootstrap standard error estimation. We discuss broad implications of this approach for causal analysis involving propensity score-based weighting. Copyright © 2018 John Wiley & Sons, Ltd.

Subaperture test of wavefront error of large telescopes: error sources and stitching performance simulations

NASA Astrophysics Data System (ADS)

Chen, Shanyong; Li, Shengyi; Wang, Guilin

2014-11-01

The wavefront error of large telescopes requires to be measured to check the system quality and also estimate the misalignment of the telescope optics including the primary, the secondary and so on. It is usually realized by a focal plane interferometer and an autocollimator flat (ACF) of the same aperture with the telescope. However, it is challenging for meter class telescopes due to high cost and technological challenges in producing the large ACF. Subaperture test with a smaller ACF is hence proposed in combination with advanced stitching algorithms. Major error sources include the surface error of the ACF, misalignment of the ACF and measurement noises. Different error sources have different impacts on the wavefront error. Basically the surface error of the ACF behaves like systematic error and the astigmatism will be cumulated and enlarged if the azimuth of subapertures remains fixed. It is difficult to accurately calibrate the ACF because it suffers considerable deformation induced by gravity or mechanical clamping force. Therefore a selfcalibrated stitching algorithm is employed to separate the ACF surface error from the subaperture wavefront error. We suggest the ACF be rotated around the optical axis of the telescope for subaperture test. The algorithm is also able to correct the subaperture tip-tilt based on the overlapping consistency. Since all subaperture measurements are obtained in the same imaging plane, lateral shift of the subapertures is always known and the real overlapping points can be recognized in this plane. Therefore lateral positioning error of subapertures has no impact on the stitched wavefront. In contrast, the angular positioning error changes the azimuth of the ACF and finally changes the systematic error. We propose an angularly uneven layout of subapertures to minimize the stitching error, which is very different from our knowledge. At last, measurement noises could never be corrected but be suppressed by means of averaging and environmental control. We simulate the performance of the stitching algorithm dealing with surface error and misalignment of the ACF, and noise suppression, which provides guidelines to optomechanical design of the stitching test system.

Optimized method for manufacturing large aspheric surfaces

NASA Astrophysics Data System (ADS)

Zhou, Xusheng; Li, Shengyi; Dai, Yifan; Xie, Xuhui

2007-12-01

Aspheric optics are being used more and more widely in modern optical systems, due to their ability of correcting aberrations, enhancing image quality, enlarging the field of view and extending the range of effect, while reducing the weight and volume of the system. With optical technology development, we have more pressing requirement to large-aperture and high-precision aspheric surfaces. The original computer controlled optical surfacing (CCOS) technique cannot meet the challenge of precision and machining efficiency. This problem has been thought highly of by researchers. Aiming at the problem of original polishing process, an optimized method for manufacturing large aspheric surfaces is put forward. Subsurface damage (SSD), full aperture errors and full band of frequency errors are all in control of this method. Lesser SSD depth can be gained by using little hardness tool and small abrasive grains in grinding process. For full aperture errors control, edge effects can be controlled by using smaller tools and amendment model with material removal function. For full band of frequency errors control, low frequency errors can be corrected with the optimized material removal function, while medium-high frequency errors by using uniform removing principle. With this optimized method, the accuracy of a K9 glass paraboloid mirror can reach rms 0.055 waves (where a wave is 0.6328μm) in a short time. The results show that the optimized method can guide large aspheric surface manufacturing effectively.

Comparing interval estimates for small sample ordinal CFA models

PubMed Central

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research. PMID:26579002

Comparing interval estimates for small sample ordinal CFA models.

PubMed

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research.

Simple and Accurate Method for Central Spin Problems

NASA Astrophysics Data System (ADS)

Lindoy, Lachlan P.; Manolopoulos, David E.

2018-06-01

We describe a simple quantum mechanical method that can be used to obtain accurate numerical results over long timescales for the spin correlation tensor of an electron spin that is hyperfine coupled to a large number of nuclear spins. This method does not suffer from the statistical errors that accompany a Monte Carlo sampling of the exact eigenstates of the central spin Hamiltonian obtained from the algebraic Bethe ansatz, or from the growth of the truncation error with time in the time-dependent density matrix renormalization group (TDMRG) approach. As a result, it can be applied to larger central spin problems than the algebraic Bethe ansatz, and for longer times than the TDMRG algorithm. It is therefore an ideal method to use to solve central spin problems, and we expect that it will also prove useful for a variety of related problems that arise in a number of different research fields.

Critique of a Hughes shuttle Ku-band data sampler/bit synchronizer

NASA Technical Reports Server (NTRS)

Holmes, J. K.

1980-01-01

An alternative bit synchronizer proposed for shuttle was analyzed in a noise-free environment by considering the basic operation of the loop via timing diagrams and by linearizing the bit synchronizer as an equivalent, continuous, phased-lock loop (PLL). The loop is composed of a high-frequency phase-frequency detector which is capable of detecting both phase and frequency errors and is used to track the clock, and a bit transition detector which attempts to track the transitions of the data bits. It was determined that the basic approach was a good design which, with proper implementation of the accumulator, up/down counter and logic should provide accurate mid-bit sampling with symmetric bits. However, when bit asymmetry occurs, the bit synchronizer can lock up with a large timing error, yet be quasi-stable (timing will not change unless the clock and bit sequence drift). This will result in incorrectly detecting some bits.

Exploration and extension of an improved Riemann track fitting algorithm

NASA Astrophysics Data System (ADS)

Strandlie, A.; Frühwirth, R.

2017-09-01

Recently, a new Riemann track fit which operates on translated and scaled measurements has been proposed. This study shows that the new Riemann fit is virtually as precise as popular approaches such as the Kalman filter or an iterative non-linear track fitting procedure, and significantly more precise than other, non-iterative circular track fitting approaches over a large range of measurement uncertainties. The fit is then extended in two directions: first, the measurements are allowed to lie on plane sensors of arbitrary orientation; second, the full error propagation from the measurements to the estimated circle parameters is computed. The covariance matrix of the estimated track parameters can therefore be computed without recourse to asymptotic properties, and is consequently valid for any number of observation. It does, however, assume normally distributed measurement errors. The calculations are validated on a simulated track sample and show excellent agreement with the theoretical expectations.

Least square regularized regression in sum space.

PubMed

Xu, Yong-Li; Chen, Di-Rong; Li, Han-Xiong; Liu, Lu

2013-04-01

This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.

Time dependent wind fields

NASA Technical Reports Server (NTRS)

Chelton, D. B.

1986-01-01

Two tasks were performed: (1) determination of the accuracy of Seasat scatterometer, altimeter, and scanning multichannel microwave radiometer measurements of wind speed; and (2) application of Seasat altimeter measurements of sea level to study the spatial and temporal variability of geostrophic flow in the Antarctic Circumpolar Current. The results of the first task have identified systematic errors in wind speeds estimated by all three satellite sensors. However, in all cases the errors are correctable and corrected wind speeds agree between the three sensors to better than 1 ms sup -1 in 96-day 2 deg. latitude by 6 deg. longitude averages. The second task has resulted in development of a new technique for using altimeter sea level measurements to study the temporal variability of large scale sea level variations. Application of the technique to the Antarctic Circumpolar Current yielded new information about the ocean circulation in this region of the ocean that is poorly sampled by conventional ship-based measurements.

Removing systematic errors in interionic potentials of mean force computed in molecular simulations using reaction-field-based electrostatics

PubMed Central

Baumketner, Andrij

2009-01-01

The performance of reaction-field methods to treat electrostatic interactions is tested in simulations of ions solvated in water. The potential of mean force between sodium chloride pair of ions and between side chains of lysine and aspartate are computed using umbrella sampling and molecular dynamics simulations. It is found that in comparison with lattice sum calculations, the charge-group-based approaches to reaction-field treatments produce a large error in the association energy of the ions that exhibits strong systematic dependence on the size of the simulation box. The atom-based implementation of the reaction field is seen to (i) improve the overall quality of the potential of mean force and (ii) remove the dependence on the size of the simulation box. It is suggested that the atom-based truncation be used in reaction-field simulations of mixed media. PMID:19292522

How to conduct External Quality Assessment Schemes for the pre-analytical phase?

PubMed

Kristensen, Gunn B B; Aakre, Kristin Moberg; Kristoffersen, Ann Helen; Sandberg, Sverre

2014-01-01

In laboratory medicine, several studies have described the most frequent errors in the different phases of the total testing process, and a large proportion of these errors occur in the pre-analytical phase. Schemes for registration of errors and subsequent feedback to the participants have been conducted for decades concerning the analytical phase by External Quality Assessment (EQA) organizations operating in most countries. The aim of the paper is to present an overview of different types of EQA schemes for the pre-analytical phase, and give examples of some existing schemes. So far, very few EQA organizations have focused on the pre-analytical phase, and most EQA organizations do not offer pre-analytical EQA schemes (EQAS). It is more difficult to perform and standardize pre-analytical EQAS and also, accreditation bodies do not ask the laboratories for results from such schemes. However, some ongoing EQA programs for the pre-analytical phase do exist, and some examples are given in this paper. The methods used can be divided into three different types; collecting information about pre-analytical laboratory procedures, circulating real samples to collect information about interferences that might affect the measurement procedure, or register actual laboratory errors and relate these to quality indicators. These three types have different focus and different challenges regarding implementation, and a combination of the three is probably necessary to be able to detect and monitor the wide range of errors occurring in the pre-analytical phase.

A method to correct sampling ghosts in historic near-infrared Fourier transform spectrometer (FTS) measurements

NASA Astrophysics Data System (ADS)

Dohe, S.; Sherlock, V.; Hase, F.; Gisi, M.; Robinson, J.; Sepúlveda, E.; Schneider, M.; Blumenstock, T.

2013-08-01

The Total Carbon Column Observing Network (TCCON) has been established to provide ground-based remote sensing measurements of the column-averaged dry air mole fractions (DMF) of key greenhouse gases. To ensure network-wide consistency, biases between Fourier transform spectrometers at different sites have to be well controlled. Errors in interferogram sampling can introduce significant biases in retrievals. In this study we investigate a two-step scheme to correct these errors. In the first step the laser sampling error (LSE) is estimated by determining the sampling shift which minimises the magnitude of the signal intensity in selected, fully absorbed regions of the solar spectrum. The LSE is estimated for every day with measurements which meet certain selection criteria to derive the site-specific time series of the LSEs. In the second step, this sequence of LSEs is used to resample all the interferograms acquired at the site, and hence correct the sampling errors. Measurements acquired at the Izaña and Lauder TCCON sites are used to demonstrate the method. At both sites the sampling error histories show changes in LSE due to instrument interventions (e.g. realignment). Estimated LSEs are in good agreement with sampling errors inferred from the ratio of primary and ghost spectral signatures in optically bandpass-limited tungsten lamp spectra acquired at Lauder. The original time series of Xair and XCO2 (XY: column-averaged DMF of the target gas Y) at both sites show discrepancies of 0.2-0.5% due to changes in the LSE associated with instrument interventions or changes in the measurement sample rate. After resampling, discrepancies are reduced to 0.1% or less at Lauder and 0.2% at Izaña. In the latter case, coincident changes in interferometer alignment may also have contributed to the residual difference. In the future the proposed method will be used to correct historical spectra at all TCCON sites.

Post-mortem clinical pharmacology

PubMed Central

Ferner, R E

2008-01-01

Clinical pharmacology assumes that deductions can be made about the concentrations of drugs from a knowledge of the pharmacokinetic parameters in an individual; and that the effects are related to the measured concentration. Post-mortem changes render the assumptions of clinical pharmacology largely invalid, and make the interpretation of concentrations measured in post-mortem samples difficult or impossible. Qualitative tests can show the presence of substances that were not present in life, and can fail to detect substances that led to death. Quantitative analysis is subject to error in itself, and because post-mortem concentrations vary in largely unpredictable ways with the site and time of sampling, as a result of the phenomenon of post-mortem redistribution. Consequently, compilations of ‘lethal concentrations’ are misleading. There is a lack of adequate studies of the true relationship between fatal events and the concentrations that can be measured subsequently, but without such studies, clinical pharmacologists and others should be wary of interpreting post-mortem measurements. PMID:18637886