Stork, LeAnna M.; Gennings, Chris; Carchman, Richard; Carter, Jr., Walter H.; Pounds, Joel G.; Mumtaz, Moiz
2006-12-01
Several assumptions, defined and undefined, are used in the toxicity assessment of chemical mixtures. In scientific practice mixture components in the low-dose region, particularly subthreshold doses, are often assumed to behave additively (i.e., zero interaction) based on heuristic arguments. This assumption has important implications in the practice of risk assessment, but has not been experimentally tested. We have developed methodology to test for additivity in the sense of Berenbaum (Advances in Cancer Research, 1981), based on the statistical equivalence testing literature where the null hypothesis of interaction is rejected for the alternative hypothesis of additivity when data support the claim. The implication of this approach is that conclusions of additivity are made with a false positive rate controlled by the experimenter. The claim of additivity is based on prespecified additivity margins, which are chosen using expert biological judgment such that small deviations from additivity, which are not considered to be biologically important, are not statistically significant. This approach is in contrast to the usual hypothesis-testing framework that assumes additivity in the null hypothesis and rejects when there is significant evidence of interaction. In this scenario, failure to reject may be due to lack of statistical power making the claim of additivity problematic. The proposed method is illustrated in a mixture of five organophosphorus pesticides that were experimentally evaluated alone and at relevant mixing ratios. Motor activity was assessed in adult male rats following acute exposure. Four low-dose mixture groups were evaluated. Evidence of additivity is found in three of the four low-dose mixture groups.The proposed method tests for additivity of the whole mixture and does not take into account subset interactions (e.g., synergistic, antagonistic) that may have occurred and cancelled each other out.
Fixed-ratio ray designs have been used for detecting and characterizing interactions of large numbers of chemicals in combination. Single chemical dose-response data are used to predict an “additivity curve” along an environmentally relevant ray. A “mixture curve” is estimated fr...
Geopositional Statistical Methods
NASA Technical Reports Server (NTRS)
Ross, Kenton
2006-01-01
RMSE based methods distort circular error estimates (up to 50% overestimation). The empirical approach is the only statistically unbiased estimator offered. Ager modification to Shultz approach is nearly unbiased, but cumbersome. All methods hover around 20% uncertainty (@ 95% confidence) for low geopositional bias error estimates. This requires careful consideration in assessment of higher accuracy products.
Statistical methods in microbiology.
Ilstrup, D M
1990-01-01
Statistical methodology is viewed by the average laboratory scientist, or physician, sometimes with fear and trepidation, occasionally with loathing, and seldom with fondness. Statistics may never be loved by the medical community, but it does not have to be hated by them. It is true that statistical science is sometimes highly mathematical, always philosophical, and occasionally obtuse, but for the majority of medical studies it can be made palatable. The goal of this article has been to outline a finite set of methods of analysis that investigators should choose based on the nature of the variable being studied and the design of the experiment. The reader is encouraged to seek the advice of a professional statistician when there is any doubt about the appropriate method of analysis. A statistician can also help the investigator with problems that have nothing to do with statistical tests, such as quality control, choice of response variable and comparison groups, randomization, and blinding of assessment of response variables. PMID:2200604
Statistical Methods in Cosmology
NASA Astrophysics Data System (ADS)
Verde, L.
2010-03-01
The advent of large data-set in cosmology has meant that in the past 10 or 20 years our knowledge and understanding of the Universe has changed not only quantitatively but also, and most importantly, qualitatively. Cosmologists rely on data where a host of useful information is enclosed, but is encoded in a non-trivial way. The challenges in extracting this information must be overcome to make the most of a large experimental effort. Even after having converged to a standard cosmological model (the LCDM model) we should keep in mind that this model is described by 10 or more physical parameters and if we want to study deviations from it, the number of parameters is even larger. Dealing with such a high dimensional parameter space and finding parameters constraints is a challenge on itself. Cosmologists want to be able to compare and combine different data sets both for testing for possible disagreements (which could indicate new physics) and for improving parameter determinations. Finally, cosmologists in many cases want to find out, before actually doing the experiment, how much one would be able to learn from it. For all these reasons, sophisiticated statistical techniques are being employed in cosmology, and it has become crucial to know some statistical background to understand recent literature in the field. I will introduce some statistical tools that any cosmologist should know about in order to be able to understand recently published results from the analysis of cosmological data sets. I will not present a complete and rigorous introduction to statistics as there are several good books which are reported in the references. The reader should refer to those.
Explorations in Statistics: Permutation Methods
ERIC Educational Resources Information Center
Curran-Everett, Douglas
2012-01-01
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eighth installment of "Explorations in Statistics" explores permutation methods, empiric procedures we can use to assess an experimental result--to test a null hypothesis--when we are reluctant to trust statistical…
Statistical Methods in Psychology Journals.
ERIC Educational Resources Information Center
Willkinson, Leland
1999-01-01
Proposes guidelines for revising the American Psychological Association (APA) publication manual or other APA materials to clarify the application of statistics in research reports. The guidelines are intended to induce authors and editors to recognize the thoughtless application of statistical methods. Contains 54 references. (SLD)
Statistical Methods for Evolutionary Trees
Edwards, A. W. F.
2009-01-01
In 1963 and 1964, L. L. Cavalli-Sforza and A. W. F. Edwards introduced novel methods for computing evolutionary trees from genetical data, initially for human populations from blood-group gene frequencies. The most important development was their introduction of statistical methods of estimation applied to stochastic models of evolution. PMID:19797062
Statistical tests of additional plate boundaries from plate motion inversions
NASA Technical Reports Server (NTRS)
Stein, S.; Gordon, R. G.
1984-01-01
The application of the F-ratio test, a standard statistical technique, to the results of relative plate motion inversions has been investigated. The method tests whether the improvement in fit of the model to the data resulting from the addition of another plate to the model is greater than that expected purely by chance. This approach appears to be useful in determining whether additional plate boundaries are justified. Previous results have been confirmed favoring separate North American and South American plates with a boundary located beween 30 N and the equator. Using Chase's global relative motion data, it is shown that in addition to separate West African and Somalian plates, separate West Indian and Australian plates, with a best-fitting boundary between 70 E and 90 E, can be resolved. These results are generally consistent with the observation that the Indian plate's internal deformation extends somewhat westward of the Ninetyeast Ridge. The relative motion pole is similar to Minster and Jordan's and predicts the NW-SE compression observed in earthquake mechanisms near the Ninetyeast Ridge.
Bond additivity corrections for quantum chemistry methods
Melius, C.F.; Allendorf, M.D.
2000-03-23
New bond additivity correction (BAC) methods have been developed for the G2 method, BAC-G2, as well as for a hybrid density functional theory (DFT) Moller-Plesset (MP)2 method, BAC-hybrid. These BAC methods use a new form of BAC corrections, involving atomic, molecular, and bond-wise additive terms. These terms enable one to treat positive and negative ions as well as neutrals. The BAC-G2 method reduces errors in the G2 method due to nearest-neighbor bonds. The parameters within the BAC-G2 method only depend on atom types. Thus the BAC-G2 method can be used to determine the parameters needed by BAC methods involving lower levels of theory, such as BAC-hybrid and BAC-MP4. The BAC-hybrid method is expected to scale well for large molecules. The BAC-hybrid method uses the differences between the DFT and MP2 predictions as an indication of the method's accuracy, whereas the BAC-G2 method uses its internal methods (G1 and G2MP2) to accomplish this. A statistical analysis of the error in each of the methods is presented on the basis of calculations performed for large sets (more than 120) of molecules.
Vortex methods and vortex statistics
Chorin, A.J.
1993-05-01
Vortex methods originated from the observation that in incompressible, inviscid, isentropic flow vorticity (or, more accurately, circulation) is a conserved quantity, as can be readily deduced from the absence of tangential stresses. Thus if the vorticity is known at time t = 0, one can deduce the flow at a later time by simply following it around. In this narrow context, a vortex method is a numerical method that makes use of this observation. Even more generally, the analysis of vortex methods leads, to problems that are closely related to problems in quantum physics and field theory, as well as in harmonic analysis. A broad enough definition of vortex methods ends up by encompassing much of science. Even the purely computational aspects of vortex methods encompass a range of ideas for which vorticity may not be the best unifying theme. The author restricts himself in these lectures to a special class of numerical vortex methods, those that are based on a Lagrangian transport of vorticity in hydrodynamics by smoothed particles (``blobs``) and those whose understanding contributes to the understanding of blob methods. Vortex methods for inviscid flow lead to systems of ordinary differential equations that can be readily clothed in Hamiltonian form, both in three and two space dimensions, and they can preserve exactly a number of invariants of the Euler equations, including topological invariants. Their viscous versions resemble Langevin equations. As a result, they provide a very useful cartoon of statistical hydrodynamics, i.e., of turbulence, one that can to some extent be analyzed analytically and more importantly, explored numerically, with important implications also for superfluids, superconductors, and even polymers. In the authors view, vortex ``blob`` methods provide the most promising path to the understanding of these phenomena.
Statistical methods in physical mapping
Nelson, D.O.
1995-05-01
One of the great success stories of modern molecular genetics has been the ability of biologists to isolate and characterize the genes responsible for serious inherited diseases like fragile X syndrome, cystic fibrosis and myotonic muscular dystrophy. This dissertation concentrates on constructing high-resolution physical maps. It demonstrates how probabilistic modeling and statistical analysis can aid molecular geneticists in the tasks of planning, execution, and evaluation of physical maps of chromosomes and large chromosomal regions. The dissertation is divided into six chapters. Chapter 1 provides an introduction to the field of physical mapping, describing the role of physical mapping in gene isolation and ill past efforts at mapping chromosomal regions. The next two chapters review and extend known results on predicting progress in large mapping projects. Such predictions help project planners decide between various approaches and tactics for mapping large regions of the human genome. Chapter 2 shows how probability models have been used in the past to predict progress in mapping projects. Chapter 3 presents new results, based on stationary point process theory, for progress measures for mapping projects based on directed mapping strategies. Chapter 4 describes in detail the construction of all initial high-resolution physical map for human chromosome 19. This chapter introduces the probability and statistical models involved in map construction in the context of a large, ongoing physical mapping project. Chapter 5 concentrates on one such model, the trinomial model. This chapter contains new results on the large-sample behavior of this model, including distributional results, asymptotic moments, and detection error rates. In addition, it contains an optimality result concerning experimental procedures based on the trinomial model. The last chapter explores unsolved problems and describes future work.
Weak additivity principle for current statistics in d dimensions.
Pérez-Espigares, C; Garrido, P L; Hurtado, P I
2016-04-01
The additivity principle (AP) allows one to compute the current distribution in many one-dimensional nonequilibrium systems. Here we extend this conjecture to general d-dimensional driven diffusive systems, and validate its predictions against both numerical simulations of rare events and microscopic exact calculations of three paradigmatic models of diffusive transport in d=2. Crucially, the existence of a structured current vector field at the fluctuating level, coupled to the local mobility, turns out to be essential to understand current statistics in d>1. We prove that, when compared to the straightforward extension of the AP to high d, the so-called weak AP always yields a better minimizer of the macroscopic fluctuation theory action for current statistics. PMID:27176236
Weak additivity principle for current statistics in d dimensions
NASA Astrophysics Data System (ADS)
Pérez-Espigares, C.; Garrido, P. L.; Hurtado, P. I.
2016-04-01
The additivity principle (AP) allows one to compute the current distribution in many one-dimensional nonequilibrium systems. Here we extend this conjecture to general d -dimensional driven diffusive systems, and validate its predictions against both numerical simulations of rare events and microscopic exact calculations of three paradigmatic models of diffusive transport in d =2 . Crucially, the existence of a structured current vector field at the fluctuating level, coupled to the local mobility, turns out to be essential to understand current statistics in d >1 . We prove that, when compared to the straightforward extension of the AP to high d , the so-called weak AP always yields a better minimizer of the macroscopic fluctuation theory action for current statistics.
Recent statistical methods for orientation data
NASA Technical Reports Server (NTRS)
Batschelet, E.
1972-01-01
The application of statistical methods for determining the areas of animal orientation and navigation are discussed. The method employed is limited to the two-dimensional case. Various tests for determining the validity of the statistical analysis are presented. Mathematical models are included to support the theoretical considerations and tables of data are developed to show the value of information obtained by statistical analysis.
Statistical methods for nuclear material management
Bowen W.M.; Bennett, C.A.
1988-12-01
This book is intended as a reference manual of statistical methodology for nuclear material management practitioners. It describes statistical methods currently or potentially important in nuclear material management, explains the choice of methods for specific applications, and provides examples of practical applications to nuclear material management problems. Together with the accompanying training manual, which contains fully worked out problems keyed to each chapter, this book can also be used as a textbook for courses in statistical methods for nuclear material management. It should provide increased understanding and guidance to help improve the application of statistical methods to nuclear material management problems.
Some useful statistical methods for model validation.
Marcus, A H; Elias, R W
1998-01-01
Although formal hypothesis tests provide a convenient framework for displaying the statistical results of empirical comparisons, standard tests should not be used without consideration of underlying measurement error structure. As part of the validation process, predictions of individual blood lead concentrations from models with site-specific input parameters are often compared with blood lead concentrations measured in field studies that also report lead concentrations in environmental media (soil, dust, water, paint) as surrogates for exposure. Measurements of these environmental media are subject to several sources of variability, including temporal and spatial sampling, sample preparation and chemical analysis, and data entry or recording. Adjustments for measurement error must be made before statistical tests can be used to empirically compare environmental data with model predictions. This report illustrates the effect of measurement error correction using a real dataset of child blood lead concentrations for an undisclosed midwestern community. We illustrate both the apparent failure of some standard regression tests and the success of adjustment of such tests for measurement error using the SIMEX (simulation-extrapolation) procedure. This procedure adds simulated measurement error to model predictions and then subtracts the total measurement error, analogous to the method of standard additions used by analytical chemists. Images Figure 1 Figure 3 PMID:9860913
Statistical methods for environmental pollution monitoring
Gilbert, R.O.
1987-01-01
The application of statistics to environmental pollution monitoring studies requires a knowledge of statistical analysis methods particularly well suited to pollution data. This book fills that need by providing sampling plans, statistical tests, parameter estimation procedure techniques, and references to pertinent publications. Most of the statistical techniques are relatively simple, and examples, exercises, and case studies are provided to illustrate procedures. The book is logically divided into three parts. Chapters 1, 2, and 3 are introductory chapters. Chapters 4 through 10 discuss field sampling designs and Chapters 11 through 18 deal with a broad range of statistical analysis procedures. Some statistical techniques given here are not commonly seen in statistics book. For example, see methods for handling correlated data (Sections 4.5 and 11.12), for detecting hot spots (Chapter 10), and for estimating a confidence interval for the mean of a lognormal distribution (Section 13.2). Also, Appendix B lists a computer code that estimates and tests for trends over time at one or more monitoring stations using nonparametric methods (Chapters 16 and 17). Unfortunately, some important topics could not be included because of their complexity and the need to limit the length of the book. For example, only brief mention could be made of time series analysis using Box-Jenkins methods and of kriging techniques for estimating spatial and spatial-time patterns of pollution, although multiple references on these topics are provided. Also, no discussion of methods for assessing risks from environmental pollution could be included.
Seasonal UK Drought Forecasting using Statistical Methods
NASA Astrophysics Data System (ADS)
Richardson, Doug; Fowler, Hayley; Kilsby, Chris; Serinaldi, Francesco
2016-04-01
In the UK drought is a recurrent feature of climate with potentially large impacts on public water supply. Water companies' ability to mitigate the impacts of drought by managing diminishing availability depends on forward planning and it would be extremely valuable to improve forecasts of drought on monthly to seasonal time scales. By focusing on statistical forecasting methods, this research aims to provide techniques that are simpler, faster and computationally cheaper than physically based models. In general, statistical forecasting is done by relating the variable of interest (some hydro-meteorological variable such as rainfall or streamflow, or a drought index) to one or more predictors via some formal dependence. These predictors are generally antecedent values of the response variable or external factors such as teleconnections. A candidate model is Generalised Additive Models for Location, Scale and Shape parameters (GAMLSS). GAMLSS is a very flexible class allowing for more general distribution functions (e.g. highly skewed and/or kurtotic distributions) and the modelling of not just the location parameter but also the scale and shape parameters. Additionally GAMLSS permits the forecasting of an entire distribution, allowing the output to be assessed in probabilistic terms rather than simply the mean and confidence intervals. Exploratory analysis of the relationship between long-memory processes (e.g. large-scale atmospheric circulation patterns, sea surface temperatures and soil moisture content) and drought should result in the identification of suitable predictors to be included in the forecasting model, and further our understanding of the drivers of UK drought.
Quantitative statistical methods for image quality assessment.
Dutta, Joyita; Ahn, Sangtae; Li, Quanzheng
2013-01-01
Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit). PMID:24312148
Quantitative Statistical Methods for Image Quality Assessment
Dutta, Joyita; Ahn, Sangtae; Li, Quanzheng
2013-01-01
Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit). PMID:24312148
Bond additivity corrections for quantum chemistry methods
C. F. Melius; M. D. Allendorf
1999-04-01
In the 1980's, the authors developed a bond-additivity correction procedure for quantum chemical calculations called BAC-MP4, which has proven reliable in calculating the thermochemical properties of molecular species, including radicals as well as stable closed-shell species. New Bond Additivity Correction (BAC) methods have been developed for the G2 method, BAC-G2, as well as for a hybrid DFT/MP2 method, BAC-Hybrid. These BAC methods use a new form of BAC corrections, involving atomic, molecular, and bond-wise additive terms. These terms enable one to treat positive and negative ions as well as neutrals. The BAC-G2 method reduces errors in the G2 method due to nearest-neighbor bonds. The parameters within the BAC-G2 method only depend on atom types. Thus the BAC-G2 method can be used to determine the parameters needed by BAC methods involving lower levels of theory, such as BAC-Hybrid and BAC-MP4. The BAC-Hybrid method should scale well for large molecules. The BAC-Hybrid method uses the differences between the DFT and MP2 as an indicator of the method's accuracy, while the BAC-G2 method uses its internal methods (G1 and G2MP2) to provide an indicator of its accuracy. Indications of the average error as well as worst cases are provided for each of the BAC methods.
A Statistical Method for Syntactic Dialectometry
ERIC Educational Resources Information Center
Sanders, Nathan C.
2010-01-01
This dissertation establishes the utility and reliability of a statistical distance measure for syntactic dialectometry, expanding dialectometry's methods to include syntax as well as phonology and the lexicon. It establishes the measure's reliability by comparing its results to those of dialectology and phonological dialectometry on Swedish…
On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis
Li, Bing; Chun, Hyonho; Zhao, Hongyu
2014-01-01
We introduce a nonparametric method for estimating non-gaussian graphical models based on a new statistical relation called additive conditional independence, which is a three-way relation among random vectors that resembles the logical structure of conditional independence. Additive conditional independence allows us to use one-dimensional kernel regardless of the dimension of the graph, which not only avoids the curse of dimensionality but also simplifies computation. It also gives rise to a parallel structure to the gaussian graphical model that replaces the precision matrix by an additive precision operator. The estimators derived from additive conditional independence cover the recently introduced nonparanormal graphical model as a special case, but outperform it when the gaussian copula assumption is violated. We compare the new method with existing ones by simulations and in genetic pathway analysis. PMID:26401064
Statistical versus nonstatistical temperature inversion methods
NASA Technical Reports Server (NTRS)
Smith, W. L.; Fleming, H. E.
1972-01-01
Vertical temperature profiles are derived from radiation measurements by inverting the integral equation of radiative transfer. Because of the nonuniqueness of the solution, the particular temperature profile obtained depends on the numerical inversion technique used and the type of auxiliary information incorporated in the solution. The choice of an inversion algorithm depends on many factors; including the speed and size of computer, the availability of representative statistics, and the accuracy of initial data. Results are presented for a numerical study comparing two contrasting inversion methods: the statistical-matrix inversion method and the nonstatistical-iterative method. These were found to be the most applicable to the problem of determining atmospheric temperature profiles. Tradeoffs between the two methods are discussed.
Effusion plate using additive manufacturing methods
Johnson, Thomas Edward; Keener, Christopher Paul; Ostebee, Heath Michael; Wegerif, Daniel Gerritt
2016-04-12
Additive manufacturing techniques may be utilized to construct effusion plates. Such additive manufacturing techniques may include defining a configuration for an effusion plate having one or more internal cooling channels. The manufacturing techniques may further include depositing a powder into a chamber, applying an energy source to the deposited powder, and consolidating the powder into a cross-sectional shape corresponding to the defined configuration. Such methods may be implemented to construct an effusion plate having one or more channels with a curved cross-sectional geometry.
Peer-Assisted Learning in Research Methods and Statistics
ERIC Educational Resources Information Center
Stone, Anna; Meade, Claire; Watling, Rosamond
2012-01-01
Feedback from students on a Level 1 Research Methods and Statistics module, studied as a core part of a BSc Psychology programme, highlighted demand for additional tutorials to help them to understand basic concepts. Students in their final year of study commonly request work experience to enhance their employability. All students on the Level 1…
Comparison of methods for computing streamflow statistics for Pennsylvania streams
Ehlke, Marla H.; Reed, Lloyd A.
1999-01-01
Methods for computing streamflow statistics intended for use on ungaged locations on Pennsylvania streams are presented and compared to frequency distributions of gaged streamflow data. The streamflow statistics used in the comparisons include the 7-day 10-year low flow, 50-year flood flow, and the 100-year flood flow; additional statistics are presented. Streamflow statistics for gaged locations on streams in Pennsylvania were computed using three methods for the comparisons: 1) Log-Pearson type III frequency distribution (Log-Pearson) of continuous-record streamflow data, 2) regional regression equations developed by the U.S. Geological Survey in 1982 (WRI 82-21), and 3) regional regression equations developed by the Pennsylvania State University in 1981 (PSU-IV). Log-Pearson distribution was considered the reference method for evaluation of the regional regression equations. Low-flow statistics were computed using the Log-Pearson distribution and WRI 82-21, whereas flood-flow statistics were computed using all three methods. The urban adjustment for PSU-IV was modified from the recommended computation to exclude Philadelphia and the surrounding areas (region 1) from the adjustment. Adjustments for storage area for PSU-IV were also slightly modified. A comparison of the 7-day 10-year low flow computed from Log-Pearson distribution and WRI-82- 21 showed that the methods produced significantly different values for about 7 percent of the state. The same methods produced 50-year and 100-year flood flows that were significantly different for about 24 percent of the state. Flood-flow statistics computed using Log-Pearson distribution and PSU-IV were not significantly different in any regions of the state. These findings are based on a statistical comparison using the t-test on signed ranks and graphical methods.
Uncertainty analysis of statistical downscaling methods
NASA Astrophysics Data System (ADS)
Khan, Mohammad Sajjad; Coulibaly, Paulin; Dibike, Yonas
2006-03-01
Three downscaling models namely Statistical Down-Scaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model have been compared in terms various uncertainty assessments exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperatures. In case of daily maximum and minimum temperature, uncertainty is assessed by comparing monthly mean and variance of downscaled and observed daily maximum and minimum temperature at each month of the year at 95% confidence level. In addition, uncertainties of the monthly means and variances of downscaled daily temperature have been calculated using 95% confidence intervals, which are compared with the observed uncertainties of means and variances. In daily precipitation downscaling, in addition to comparing means and variances, uncertainties have been assessed by comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions (cdfs) of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and downscaled daily precipitation. The study has been carried out using 40 years of observed and downscaled daily precipitation, daily maximum and minimum temperature data using NCEP (National Center for Environmental Prediction) reanalysis predictors starting from 1961 to 2000. The uncertainty assessment results indicate that the SDSM is the most capable of reproducing various statistical characteristics of observed data in its downscaled results with 95% confidence level, the ANN is the least capable in this respect, and the LARS-WG is in between SDSM and ANN.
Supplier Selection Using Weighted Utility Additive Method
NASA Astrophysics Data System (ADS)
Karande, Prasad; Chakraborty, Shankar
2015-10-01
Supplier selection is a multi-criteria decision-making (MCDM) problem which mainly involves evaluating a number of available suppliers according to a set of common criteria for choosing the best one to meet the organizational needs. For any manufacturing or service organization, selecting the right upstream suppliers is a key success factor that will significantly reduce purchasing cost, increase downstream customer satisfaction and improve competitive ability. The past researchers have attempted to solve the supplier selection problem employing different MCDM techniques which involve active participation of the decision makers in the decision-making process. This paper deals with the application of weighted utility additive (WUTA) method for solving supplier selection problems. The WUTA method, an extension of utility additive approach, is based on ordinal regression and consists of building a piece-wise linear additive decision model from a preference structure using linear programming (LP). It adopts preference disaggregation principle and addresses the decision-making activities through operational models which need implicit preferences in the form of a preorder of reference alternatives or a subset of these alternatives present in the process. The preferential preorder provided by the decision maker is used as a restriction of a LP problem, which has its own objective function, minimization of the sum of the errors associated with the ranking of each alternative. Based on a given reference ranking of alternatives, one or more additive utility functions are derived. Using these utility functions, the weighted utilities for individual criterion values are combined into an overall weighted utility for a given alternative. It is observed that WUTA method, having a sound mathematical background, can provide accurate ranking to the candidate suppliers and choose the best one to fulfill the organizational requirements. Two real time examples are illustrated to prove
Computational Statistical Methods for Social Network Models
Hunter, David R.; Krivitsky, Pavel N.; Schweinberger, Michael
2013-01-01
We review the broad range of recent statistical work in social network models, with emphasis on computational aspects of these methods. Particular focus is applied to exponential-family random graph models (ERGM) and latent variable models for data on complete networks observed at a single time point, though we also briefly review many methods for incompletely observed networks and networks observed at multiple time points. Although we mention far more modeling techniques than we can possibly cover in depth, we provide numerous citations to current literature. We illustrate several of the methods on a small, well-known network dataset, Sampson’s monks, providing code where possible so that these analyses may be duplicated. PMID:23828720
Statistical methods of parameter estimation for deterministically chaotic time series.
Pisarenko, V F; Sornette, D
2004-03-01
We discuss the possibility of applying some standard statistical methods (the least-square method, the maximum likelihood method, and the method of statistical moments for estimation of parameters) to deterministically chaotic low-dimensional dynamic system (the logistic map) containing an observational noise. A "segmentation fitting" maximum likelihood (ML) method is suggested to estimate the structural parameter of the logistic map along with the initial value x(1) considered as an additional unknown parameter. The segmentation fitting method, called "piece-wise" ML, is similar in spirit but simpler and has smaller bias than the "multiple shooting" previously proposed. Comparisons with different previously proposed techniques on simulated numerical examples give favorable results (at least, for the investigated combinations of sample size N and noise level). Besides, unlike some suggested techniques, our method does not require the a priori knowledge of the noise variance. We also clarify the nature of the inherent difficulties in the statistical analysis of deterministically chaotic time series and the status of previously proposed Bayesian approaches. We note the trade off between the need of using a large number of data points in the ML analysis to decrease the bias (to guarantee consistency of the estimation) and the unstable nature of dynamical trajectories with exponentially fast loss of memory of the initial condition. The method of statistical moments for the estimation of the parameter of the logistic map is discussed. This method seems to be the unique method whose consistency for deterministically chaotic time series is proved so far theoretically (not only numerically). PMID:15089376
A new method for derivation of statistical weight of the Gentile Statistics
NASA Astrophysics Data System (ADS)
Selvi, Sevilay; Uncu, Haydar
2015-10-01
We present a new method for obtaining the statistical weight of the Gentile Statistics. In a recent paper, Perez and Tun presented an approximate combinatoric and an exact recursive formula for the statistical weight of Gentile Statistics, beginning from bosonic and fermionic cases, respectively Hernandez-Perez and Tun (2007). In this paper, we obtain two exact, one combinatoric and one recursive, formulae for the statistical weight of Gentile Statistics, by another approach. The combinatoric formula is valid only for special cases, whereas recursive formula is valid for all possible cases. Moreover, for a given q-maximum number of particles that can occupy a level for Gentile statistics-the recursive formula we have derived gives the result much faster than the recursive formula presented in Hernandez-Perez and Tun (2007), when one uses a computer program. Moreover we obtained the statistical weight for the distribution proposed by Dai and Xie (2009).
Statistical methods of estimating mining costs
Long, K.R.
2011-01-01
Until it was defunded in 1995, the U.S. Bureau of Mines maintained a Cost Estimating System (CES) for prefeasibility-type economic evaluations of mineral deposits and estimating costs at producing and non-producing mines. This system had a significant role in mineral resource assessments to estimate costs of developing and operating known mineral deposits and predicted undiscovered deposits. For legal reasons, the U.S. Geological Survey cannot update and maintain CES. Instead, statistical tools are under development to estimate mining costs from basic properties of mineral deposits such as tonnage, grade, mineralogy, depth, strip ratio, distance from infrastructure, rock strength, and work index. The first step was to reestimate "Taylor's Rule" which relates operating rate to available ore tonnage. The second step was to estimate statistical models of capital and operating costs for open pit porphyry copper mines with flotation concentrators. For a sample of 27 proposed porphyry copper projects, capital costs can be estimated from three variables: mineral processing rate, strip ratio, and distance from nearest railroad before mine construction began. Of all the variables tested, operating costs were found to be significantly correlated only with strip ratio.
Statistical Methods Used in Gifted Education Journals, 2006-2010
ERIC Educational Resources Information Center
Warne, Russell T.; Lazo, Maria; Ramos, Tammy; Ritter, Nicola
2012-01-01
This article describes the statistical methods used in quantitative and mixed methods articles between 2006 and 2010 in five gifted education research journals. Results indicate that the most commonly used statistical methods are means (85.9% of articles), standard deviations (77.8%), Pearson's "r" (47.8%), X[superscript 2] (32.2%), ANOVA (30.7%),…
Problems of applicability of statistical methods in cosmology
Levin, S. F.
2015-12-15
The problems arising from the incorrect formulation of measuring problems of identification for cosmological models and violations of conditions of applicability of statistical methods are considered.
Liu, Yang; Vijver, Martina G; Qiu, Hao; Baas, Jan; Peijnenburg, Willie J G M
2015-12-01
There is increasing attention from scientists and policy makers to the joint effects of multiple metals on organisms when present in a mixture. Using root elongation of lettuce (Lactuca sativa L.) as a toxicity endpoint, the combined effects of binary mixtures of Cu, Cd, and Ni were studied. The statistical MixTox model was used to search deviations from the reference models i.e. concentration addition (CA) and independent action (IA). The deviations were subsequently interpreted as 'interactions'. A comprehensive experiment was designed to test the reproducibility of the 'interactions'. The results showed that the toxicity of binary metal mixtures was equally well predicted by both reference models. We found statistically significant 'interactions' in four of the five total datasets. However, the patterns of 'interactions' were found to be inconsistent or even contradictory across the different independent experiments. It is recommended that a statistically significant 'interaction', must be treated with care and is not necessarily biologically relevant. Searching a statistically significant interaction can be the starting point for further measurements and modeling to advance the understanding of underlying mechanisms and non-additive interactions occurring inside the organisms. PMID:26188643
Cratering statistics on asteroids: Methods and perspectives
NASA Astrophysics Data System (ADS)
Chapman, C.
2014-07-01
Crater size-frequency distributions (SFDs) on the surfaces of solid-surfaced bodies in the solar system have provided valuable insights about planetary surface processes and about impactor populations since the first spacecraft images were obtained in the 1960s. They can be used to determine relative age differences between surficial units, to obtain absolute model ages if the impactor flux and scaling laws are understood, to assess various endogenic planetary or asteroidal processes that degrade craters or resurface units, as well as assess changes in impactor populations across the solar system and/or with time. The first asteroid SFDs were measured from Galileo images of Gaspra and Ida (cf., Chapman 2002). Despite the superficial simplicity of these studies, they are fraught with many difficulties, including confusion by secondary and/or endogenic cratering and poorly understood aspects of varying target properties (including regoliths, ejecta blankets, and nearly-zero-g rubble piles), widely varying attributes of impactors, and a host of methodological problems including recognizability of degraded craters, which is affected by illumination angle and by the ''personal equations'' of analysts. Indeed, controlled studies (Robbins et al. 2014) demonstrate crater-density differences of a factor of two or more between experienced crater counters. These inherent difficulties have been especially apparent in divergent results for Vesta from different members of the Dawn Science Team (cf. Russell et al. 2013). Indeed, they have been exacerbated by misuse of a widely available tool (Craterstats: hrscview.fu- berlin.de/craterstats.html), which incorrectly computes error bars for proper interpretation of cumulative SFDs, resulting in derived model ages specified to three significant figures and interpretations of statistically insignificant kinks. They are further exacerbated, and for other small-body crater SFDs analyzed by the Berlin group, by stubbornly adopting
A whirlwind tour of statistical methods in structural dynamics.
Booker, J. M.
2004-01-01
Several statistical methods and their corresponding principles of application to structural dynamics problems will be presented. This set was chosen based upon the projects and their corresponding challenges in the Engineering Sciences & Applications (ESA) Division at Los Alamos National Laboratory and focuses on variance-based uncertainty quantification. Our structural dynamics applications are heavily involved in modeling and simulation, often with sparse data availability. In addition to models, heavy reliance is placed upon the use of expertise and experience. Beginning with principles of inference and prediction, some statistical tools for verification and validation are introduced. Among these are the principles of good experimental design for test and model computation planning, and the combination of data, models and knowledge through the use of Bayes Theorem. A brief introduction to multivariate methods and exploratory data analysis will be presented as part of understanding relationships and variation among important parameters, physical quantities of interest, measurements, inputs and outputs. Finally, the use of these methods and principles will be discussed in drawing conclusions from the validation assessment process under uncertainty.
Hybrid perturbation methods based on statistical time series models
NASA Astrophysics Data System (ADS)
San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario
2016-04-01
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.
Online Statistics Labs in MSW Research Methods Courses: Reducing Reluctance toward Statistics
ERIC Educational Resources Information Center
Elliott, William; Choi, Eunhee; Friedline, Terri
2013-01-01
This article presents results from an evaluation of an online statistics lab as part of a foundations research methods course for master's-level social work students. The article discusses factors that contribute to an environment in social work that fosters attitudes of reluctance toward learning and teaching statistics in research methods…
An Introductory Overview of Statistical Methods for Discrete Time Series
NASA Astrophysics Data System (ADS)
Meng, X.-L.; California-Harvard AstroStat Collaboration
2004-08-01
A number of statistical problems encounted in astrophysics are concerned with discrete time series, such as photon counts with variation in source intensity over time. This talk provides an introductory overview of the current state-of-the-art methods in statistics, including Bayesian methods aided by Markov chain Monte Carlo, for modeling and analyzing such data. These methods have also been successfully applied in other fields, such as economics.
Statistical and Computational Methods for Genetic Diseases: An Overview
Di Taranto, Maria Donata
2015-01-01
The identification of causes of genetic diseases has been carried out by several approaches with increasing complexity. Innovation of genetic methodologies leads to the production of large amounts of data that needs the support of statistical and computational methods to be correctly processed. The aim of the paper is to provide an overview of statistical and computational methods paying attention to methods for the sequence analysis and complex diseases. PMID:26106440
Evaluating Three Different Methods of Determining Addition in Presbyopia
Yazdani, Negareh; Khorasani, Abbas Azimi; Moghadam, Hanieh Mirhajian; Yekta, Abbas Ali; Ostadimoghaddam, Hadi; Shandiz, Javad Heravian
2016-01-01
Purpose: To compare three different methods for determining addition in presbyopes. Methods: The study included 81 subjects with presbyopia who aged 40-70 years. Reading addition values were measured using 3 approaches including the amplitude of accommodation (AA), dynamic retinoscopy (DR), and increasing plus lens (IPL). Results: IPL overestimated reading addition relative to other methods. Mean near addition obtained by AA, DR and IPL were 1.31, 1.68 and 1.77, respectively. Our results showed that IPL method could provide 20/20 vision at near in the majority of presbyopic subjects (63.4%). Conclusion: The results were approximately the same for 3 methods and provided comparable final addition; however, mean near additions were higher with increasing plus lens compared with the other two methods. In presbyopic individuals, increasing plus lens is recommended as the least time-consuming method with the range of ±0.50 diopter at the 40 cm working distance. PMID:27621785
10 CFR 2.705 - Discovery-additional methods.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 10 Energy 1 2011-01-01 2011-01-01 false Discovery-additional methods. 2.705 Section 2.705 Energy NUCLEAR REGULATORY COMMISSION RULES OF PRACTICE FOR DOMESTIC LICENSING PROCEEDINGS AND ISSUANCE OF ORDERS Rules for Formal Adjudications § 2.705 Discovery-additional methods. (a) Discovery methods. Parties may obtain discovery by one or more of...
Statistical Morphometry of Small Martian Craters: New Methods and Results
NASA Astrophysics Data System (ADS)
Watters, W. A.; Geiger, L.; Fendrock, M.; Gibson, R.; Radford, A.
2015-05-01
Methods for automatic morphometric characterization of craters for large statistical studies; measured dependence of shape on size, terrain, modification, and velocity (via primary-to-secondary distance); evaluation of Ames Stereo Pipeline DEMs.
Development and testing of improved statistical wind power forecasting methods.
Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J.
2011-12-06
Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios
Petersson, K M; Nichols, T E; Poline, J B; Holmes, A P
1999-01-01
Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference. PMID:10466149
Using the statistical analysis method to assess the landslide susceptibility
NASA Astrophysics Data System (ADS)
Chan, Hsun-Chuan; Chen, Bo-An; Wen, Yo-Ting
2015-04-01
This study assessed the landslide susceptibility in Jing-Shan River upstream watershed, central Taiwan. The landslide inventories during typhoons Toraji in 2001, Mindulle in 2004, Kalmaegi and Sinlaku in 2008, Morakot in 2009, and the 0719 rainfall event in 2011, which were established by Taiwan Central Geological Survey, were used as landslide data. This study aims to assess the landslide susceptibility by using different statistical methods including logistic regression, instability index method and support vector machine (SVM). After the evaluations, the elevation, slope, slope aspect, lithology, terrain roughness, slope roughness, plan curvature, profile curvature, total curvature, average of rainfall were chosen as the landslide factors. The validity of the three established models was further examined by the receiver operating characteristic curve. The result of logistic regression showed that the factor of terrain roughness and slope roughness had a stronger impact on the susceptibility value. Instability index method showed that the factor of terrain roughness and lithology had a stronger impact on the susceptibility value. Due to the fact that the use of instability index method may lead to possible underestimation around the river side. In addition, landslide susceptibility indicated that the use of instability index method laid a potential issue about the number of factor classification. An increase of the number of factor classification may cause excessive variation coefficient of the factor. An decrease of the number of factor classification may make a large range of nearby cells classified into the same susceptibility level. Finally, using the receiver operating characteristic curve discriminate the three models. SVM is a preferred method than the others in assessment of landslide susceptibility. Moreover, SVM is further suggested to be nearly logistic regression in terms of recognizing the medium-high and high susceptibility.
Methods for detecting additional genes underlying Alzheimer disease
Locke, P.A.; Haines, J.L.; Ter-Minassian, M.
1994-09-01
Alzheimer`s disease (AD) is a complex inherited disorder with proven genetic heterogeneity. To date, genes on chromosome 21 (APP) and 14 (not yet identified) are associated with early-onset familial AD, while the APOE gene on chromosome 19 is associated with both late onset familial and sporadic AD and early onset sporadic AD. Although these genes likely account for the majority of AD, many familial cases cannot be traced to any of these genes. From a set of 127 late-onset multiplex families screened for APOE, 43 (34%) families have at least one affected individual with no APOE-4 allele, suggesting an alternative genetic etiology. Simulation studies indicated that additional loci could be identified through a genomic screen with a 10 cM sieve on a subset of 21 well documented, non-APOE-4 families. Given the uncertainties in the mode of inheritance, reliance on a single analytical method could result in a missed linkage. Therefore, we have developed a strategy of using multiple overlapping yet complementary methods to detect linkage. These include sib-pair analysis and affected-pedigree-member analysis, neither of which makes assumptions about mode of inheritance, and lod score analysis (using two predefined genetic models). In order for a marker to qualify for follow-up, it must fit at least two of three criteria. These are nominal P values of 0.05 or less for the non-parametric methods, and/or a lod score greater than 1.0. Adjacent markers each fulfilling a single criterion also warrant follow-up. To date, we have screened 61 markers on chromosomes 1, 2, 3, 18, 19, 21, and 22. One marker, D2S163, generated a lod score of 1.06 ({theta} = 0.15) and an APMT statistic of 3.68 (P < 0.001). This region is currently being investigated in more detail. Updated results of this region plus additional screening data will be presented.
Applications of computer-intensive statistical methods to environmental research.
Pitt, D G; Kreutzweiser, D P
1998-02-01
Conventional statistical approaches rely heavily on the properties of the central limit theorem to bridge the gap between the characteristics of a sample and some theoretical sampling distribution. Problems associated with nonrandom sampling, unknown population distributions, heterogeneous variances, small sample sizes, and missing data jeopardize the assumptions of such approaches and cast skepticism on conclusions. Conventional nonparametric alternatives offer freedom from distribution assumptions, but design limitations and loss of power can be serious drawbacks. With the data-processing capacity of today's computers, a new dimension of distribution-free statistical methods has evolved that addresses many of the limitations of conventional parametric and nonparametric methods. Computer-intensive statistical methods involve reshuffling, resampling, or simulating a data set thousands of times to empirically define a sampling distribution for a chosen test statistic. The only assumption necessary for valid results is the random assignment of experimental units to the test groups or treatments. Application to a real data set illustrates the advantages of these methods, including freedom from distribution assumptions without loss of power, complete choice over test statistics, easy adaptation to design complexities and missing data, and considerable intuitive appeal. The illustrations also reveal that computer-intensive methods can be more time consuming than conventional methods and the amount of computer code required to orchestrate reshuffling, resampling, or simulation procedures can be appreciable. PMID:9515080
Scene-based nonuniformity correction method using multiscale constant statistics
NASA Astrophysics Data System (ADS)
Zuo, Chao; Chen, Qian; Gu, Guohua; Sui, Xiubao; Qian, Weixian
2011-08-01
In scene-based nonuniformity correction (NUC) methods for infrared focal plane array cameras, the statistical approaches have been well studied because of their lower computational complexity. However, when the assumptions imposed by statistical algorithms are violated, their performance is poor. Moreover, many of these techniques, like the global constant statistics method, usually need tens of thousands of image frames to obtain a good NUC result. In this paper, we introduce a new statistical NUC method called the multiscale constant statistics (MSCS). The MSCS statically considers that the spatial scale of the temporal constant distribution expands over time. Under the assumption that the nonuniformity is distributed in a higher spatial frequency domain, the spatial range for gain and offset estimates gradually expands to guarantee fast compensation for nonuniformity. Furthermore, an exponential window and a tolerance interval for the acquired data are introduced to capture the drift in nonuniformity and eliminate the ghosting artifacts. The strength of the proposed method lies in its simplicity, low computational complexity, and its good trade-off between convergence rate and correction precision. The NUC ability of the proposed method is demonstrated by using infrared video sequences with both synthetic and real nonuniformity.
Statistical Methods for Establishing Personalized Treatment Rules in Oncology
Ma, Junsheng; Hobbs, Brian P.; Stingo, Francesco C.
2015-01-01
The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. A wide variety of methods have been developed. However, heretofore the usefulness of these recent advances has not been fully recognized by the oncology community, and the scope of their applications has not been summarized. In this paper, we provide an overview of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. We also point the reader to statistical software for implementation of the methods when available. PMID:26446492
Statistical method of evaluation of flip-flop dynamical parameters
NASA Astrophysics Data System (ADS)
Wieczorek, P. Z.; Opalski, L. J.
2008-01-01
This paper presents statistical algorithm and measurement system for precise evaluation of flip-flop dynamical parameters in asynchronous operation. The analyzed flip-flop parameters are failure probability, MTBF and propagation delay. It is shown how these parameters depend on metastable operation of flip-flops. The numerical and hardware solutions shown in article allow for precise and reliable comparison of flip-flops. Also the analysis of influence of flip-flop electrical parameters of flip-flop electrical parameters on their metastable operation is possible with use of presented statistical method. Statistical estimation of parameters of flip-flops in which metastability occurs, seems to be more reliable than standard empirical methods of flip-flop analysis. Presented method allows for showing inaccuracies in theoretical model of metastability.
Statistical methods for detecting periodic fragments in DNA sequence data
2011-01-01
Background Period 10 dinucleotides are structurally and functionally validated factors that influence the ability of DNA to form nucleosomes, histone core octamers. Robust identification of periodic signals in DNA sequences is therefore required to understand nucleosome organisation in genomes. While various techniques for identifying periodic components in genomic sequences have been proposed or adopted, the requirements for such techniques have not been considered in detail and confirmatory testing for a priori specified periods has not been developed. Results We compared the estimation accuracy and suitability for confirmatory testing of autocorrelation, discrete Fourier transform (DFT), integer period discrete Fourier transform (IPDFT) and a previously proposed Hybrid measure. A number of different statistical significance procedures were evaluated but a blockwise bootstrap proved superior. When applied to synthetic data whose period-10 signal had been eroded, or for which the signal was approximately period-10, the Hybrid technique exhibited superior properties during exploratory period estimation. In contrast, confirmatory testing using the blockwise bootstrap procedure identified IPDFT as having the greatest statistical power. These properties were validated on yeast sequences defined from a ChIP-chip study where the Hybrid metric confirmed the expected dominance of period-10 in nucleosome associated DNA but IPDFT identified more significant occurrences of period-10. Application to the whole genomes of yeast and mouse identified ~ 21% and ~ 19% respectively of these genomes as spanned by period-10 nucleosome positioning sequences (NPS). Conclusions For estimating the dominant period, we find the Hybrid period estimation method empirically to be the most effective for both eroded and approximate periodicity. The blockwise bootstrap was found to be effective as a significance measure, performing particularly well in the problem of period detection in the
Knowledge acquisition for expert systems using statistical methods
NASA Technical Reports Server (NTRS)
Belkin, Brenda L.; Stengel, Robert F.
1991-01-01
A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. A statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors is presented. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data is described. The development method invokes two statistical techniques: Analysis-of-Variance (ANOVA) and the ID3 algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithm identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set.
Landslide Susceptibility Statistical Methods: A Critical and Systematic Literature Review
NASA Astrophysics Data System (ADS)
Mihir, Monika; Malamud, Bruce; Rossi, Mauro; Reichenbach, Paola; Ardizzone, Francesca
2014-05-01
Landslide susceptibility assessment, the subject of this systematic review, is aimed at understanding the spatial probability of slope failures under a set of geomorphological and environmental conditions. It is estimated that about 375 landslides that occur globally each year are fatal, with around 4600 people killed per year. Past studies have brought out the increasing cost of landslide damages which primarily can be attributed to human occupation and increased human activities in the vulnerable environments. Many scientists, to evaluate and reduce landslide risk, have made an effort to efficiently map landslide susceptibility using different statistical methods. In this paper, we do a critical and systematic landslide susceptibility literature review, in terms of the different statistical methods used. For each of a broad set of studies reviewed we note: (i) study geography region and areal extent, (ii) landslide types, (iii) inventory type and temporal period covered, (iv) mapping technique (v) thematic variables used (vi) statistical models, (vii) assessment of model skill, (viii) uncertainty assessment methods, (ix) validation methods. We then pulled out broad trends within our review of landslide susceptibility, particularly regarding the statistical methods. We found that the most common statistical methods used in the study of landslide susceptibility include logistic regression, artificial neural network, discriminant analysis and weight of evidence. Although most of the studies we reviewed assessed the model skill, very few assessed model uncertainty. In terms of geographic extent, the largest number of landslide susceptibility zonations were in Turkey, Korea, Spain, Italy and Malaysia. However, there are also many landslides and fatalities in other localities, particularly India, China, Philippines, Nepal and Indonesia, Guatemala, and Pakistan, where there are much fewer landslide susceptibility studies available in the peer-review literature. This
Conventional and Newer Statistical Methods in Meta-Analysis.
ERIC Educational Resources Information Center
Kulik, James A.; Kulik, Chen-Lin C.
The assumptions and consequences of applying conventional and newer statistical methods to meta-analytic data sets are reviewed. The application of the two approaches to a meta-analytic data set described by L. V. Hedges (1984) illustrates the differences. Hedges analyzed six studies of the effects of open education on student cooperation. The…
Optimal Multicomponent Analysis Using the Generalized Standard Addition Method.
ERIC Educational Resources Information Center
Raymond, Margaret; And Others
1983-01-01
Describes an experiment on the simultaneous determination of chromium and magnesium by spectophotometry modified to include the Generalized Standard Addition Method computer program, a multivariate calibration method that provides optimal multicomponent analysis in the presence of interference and matrix effects. Provides instructions for…
System and method for statistically monitoring and analyzing sensed conditions
Pebay, Philippe P.; Brandt, James M. , Gentile; Ann C. , Marzouk; Youssef M. , Hale; Darrian J. , Thompson; David C.
2010-07-13
A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.
System and method for statistically monitoring and analyzing sensed conditions
Pebay, Philippe P.; Brandt, James M.; Gentile, Ann C.; Marzouk, Youssef M.; Hale, Darrian J.; Thompson, David C.
2011-01-25
A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.
System and method for statistically monitoring and analyzing sensed conditions
Pebay, Philippe P.; Brandt, James M.; Gentile, Ann C.; Marzouk, Youssef M.; Hale, Darrian J.; Thompson, David C.
2011-01-04
A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.
NASA Technical Reports Server (NTRS)
Smalheer, C. V.
1973-01-01
The chemistry of lubricant additives is discussed to show what the additives are chemically and what functions they perform in the lubrication of various kinds of equipment. Current theories regarding the mode of action of lubricant additives are presented. The additive groups discussed include the following: (1) detergents and dispersants, (2) corrosion inhibitors, (3) antioxidants, (4) viscosity index improvers, (5) pour point depressants, and (6) antifouling agents.
Predicting recreational water quality advisories: A comparison of statistical methods
Brooks, Wesley R.; Corsi, Steven R.; Fienen, Michael N.; Carvin, Rebecca B.
2016-01-01
Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.
Colon-Berlingeri, Migdalisel; Burrowes, Patricia A
2011-01-01
Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the undergraduate biology curriculum. The curricular revision included changes in the suggested course sequence, addition of statistics and precalculus as prerequisites to core science courses, and incorporating interdisciplinary (math-biology) learning activities in genetics and zoology courses. In this article, we describe the activities developed for these two courses and the assessment tools used to measure the learning that took place with respect to biology and statistics. We distinguished the effectiveness of these learning opportunities in helping students improve their understanding of the math and statistical concepts addressed and, more importantly, their ability to apply them to solve a biological problem. We also identified areas that need emphasis in both biology and mathematics courses. In light of our observations, we recommend best practices that biology and mathematics academic departments can implement to train undergraduates for the demands of modern biology. PMID:21885822
Colon-Berlingeri, Migdalisel; Burrowes, Patricia A.
2011-01-01
Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the undergraduate biology curriculum. The curricular revision included changes in the suggested course sequence, addition of statistics and precalculus as prerequisites to core science courses, and incorporating interdisciplinary (math–biology) learning activities in genetics and zoology courses. In this article, we describe the activities developed for these two courses and the assessment tools used to measure the learning that took place with respect to biology and statistics. We distinguished the effectiveness of these learning opportunities in helping students improve their understanding of the math and statistical concepts addressed and, more importantly, their ability to apply them to solve a biological problem. We also identified areas that need emphasis in both biology and mathematics courses. In light of our observations, we recommend best practices that biology and mathematics academic departments can implement to train undergraduates for the demands of modern biology. PMID:21885822
Additive manufacturing method for SRF components of various geometries
Rimmer, Robert; Frigola, Pedro E; Murokh, Alex Y
2015-05-05
An additive manufacturing method for forming nearly monolithic SRF niobium cavities and end group components of arbitrary shape with features such as optimized wall thickness and integral stiffeners, greatly reducing the cost and technical variability of conventional cavity construction. The additive manufacturing method for forming an SRF cavity, includes atomizing niobium to form a niobium powder, feeding the niobium powder into an electron beam melter under a vacuum, melting the niobium powder under a vacuum in the electron beam melter to form an SRF cavity; and polishing the inside surface of the SRF cavity.
Statistical inference for the additive hazards model under outcome-dependent sampling
Yu, Jichang; Liu, Yanyan; Sandler, Dale P.; Zhou, Haibo
2015-01-01
Cost-effective study design and proper inference procedures for data from such designs are always of particular interests to study investigators. In this article, we propose a biased sampling scheme, an outcome-dependent sampling (ODS) design for survival data with right censoring under the additive hazards model. We develop a weighted pseudo-score estimator for the regression parameters for the proposed design and derive the asymptotic properties of the proposed estimator. We also provide some suggestions for using the proposed method by evaluating the relative efficiency of the proposed method against simple random sampling design and derive the optimal allocation of the subsamples for the proposed design. Simulation studies show that the proposed ODS design is more powerful than other existing designs and the proposed estimator is more efficient than other estimators. We apply our method to analyze a cancer study conducted at NIEHS, the Cancer Incidence and Mortality of Uranium Miners Study, to study the risk of radon exposure to cancer. PMID:26379363
Statistical approaches to pharmacodynamic modeling: motivations, methods, and misperceptions.
Mick, R; Ratain, M J
1993-01-01
We have attempted to outline the fundamental statistical aspects of pharmacodynamic modeling. Unexpected yet substantial variability in effect in a group of similarly treated patients is the key motivation for pharmacodynamic investigations. Pharmacokinetic and/or pharmacodynamic factors may influence this variability. Residual variability in effect that persists after accounting for drug exposure indicates that further statistical modeling with pharmacodynamic factors is warranted. Factors that significantly predict interpatient variability in effect may then be employed to individualize the drug dose. In this paper we have emphasized the need to understand the properties of the effect measure and explanatory variables in terms of scale, distribution, and statistical relationship. The assumptions that underlie many types of statistical models have been discussed. The role of residual analysis has been stressed as a useful method to verify assumptions. We have described transformations and alternative regression methods that are employed when these assumptions are found to be in violation. Sequential selection procedures for the construction of multivariate models have been presented. The importance of assessing model performance has been underscored, most notably in terms of bias and precision. In summary, pharmacodynamic analyses are now commonly performed and reported in the oncologic literature. The content and format of these analyses has been variable. The goals of such analyses are to identify and describe pharmacodynamic relationships and, in many cases, to propose a statistical model. However, the appropriateness and performance of the proposed model are often difficult to judge. Table 1 displays suggestions (in a checklist format) for structuring the presentation of pharmacodynamic analyses, which reflect the topics reviewed in this paper. PMID:8269582
Review of Statistical Methods for Analysing Healthcare Resources and Costs
Mihaylova, Borislava; Briggs, Andrew; O'Hagan, Anthony; Thompson, Simon G
2011-01-01
We review statistical methods for analysing healthcare resource use and costs, their ability to address skewness, excess zeros, multimodality and heavy right tails, and their ease for general use. We aim to provide guidance on analysing resource use and costs focusing on randomised trials, although methods often have wider applicability. Twelve broad categories of methods were identified: (I) methods based on the normal distribution, (II) methods following transformation of data, (III) single-distribution generalized linear models (GLMs), (IV) parametric models based on skewed distributions outside the GLM family, (V) models based on mixtures of parametric distributions, (VI) two (or multi)-part and Tobit models, (VII) survival methods, (VIII) non-parametric methods, (IX) methods based on truncation or trimming of data, (X) data components models, (XI) methods based on averaging across models, and (XII) Markov chain methods. Based on this review, our recommendations are that, first, simple methods are preferred in large samples where the near-normality of sample means is assured. Second, in somewhat smaller samples, relatively simple methods, able to deal with one or two of above data characteristics, may be preferable but checking sensitivity to assumptions is necessary. Finally, some more complex methods hold promise, but are relatively untried; their implementation requires substantial expertise and they are not currently recommended for wider applied work. Copyright © 2010 John Wiley & Sons, Ltd. PMID:20799344
Yang-Yang Equilibrium Statistical Mechanics: A Brilliant Method
NASA Astrophysics Data System (ADS)
Guan, Xi-Wen; Chen, Yang-Yang
C. N. Yang and C. P. Yang in 1969 (J. Math. Phys. 10, 1115 (1969)) for the first time proposed a rigorous approach to the thermodynamics of the one-dimensional system of bosons with a delta-function interaction. This paper was a breakthrough in exact statistical mechanics, after C. N. Yang (Phys. Rev. Lett. 19, 1312 (1967)) published his seminal work on the discovery of the Yang-Baxter equation in 1967. Yang and Yang's brilliant method yields significant applications in a wide range of fields of physics. In this communication, we briefly introduce the method of the Yang-Yang equilibrium statistical mechanics and demonstrate a fundamental application of the Yang-Yang method for the study of thermodynamics of the Lieb-Liniger model with strong and weak interactions in a whole temperature regime. We also consider the equivalence between the Yang-Yang's thermodynamic Bethe ansatz equation and the thermodynamics of the ideal gas with the Haldane's generalized exclusion statistics.
Yang-Yang equilibrium statistical mechanics: A brilliant method
NASA Astrophysics Data System (ADS)
Guan, Xi-Wen; Chen, Yang-Yang
2016-03-01
Yang and Yang in 1969 [J. Math. Phys. 10, 1115 (1969)] for the first time proposed a rigorous approach to the thermodynamics of the one-dimensional system of bosons with a delta-function interaction. This paper was a breakthrough in exact statistical mechanics, after Yang [Phys. Rev. Lett. 19, 1312 (1967)] published his seminal work on the discovery of the Yang-Baxter equation in 1967. Yang and Yang’s brilliant method yields significant applications in a wide range of fields of physics. In this paper, we briefly introduce the method of the Yang-Yang equilibrium statistical mechanics and demonstrate a fundamental application of the Yang-Yang method for the study of thermodynamics of the Lieb-Liniger model with strong and weak interactions in a whole temperature regime. We also consider the equivalence between the Yang-Yang’s thermodynamic Bethe ansatz equation and the thermodynamics of the ideal gas with the Haldane’s generalized exclusion statistics.
Selvidge, J.E.
1982-06-01
Recent literature in the field of enhanced oil recovery (EOR) was surveyed to determine the extent to which researchers in EOR take advantage of statistical techniques in analyzing their data. In addition to determining the current level of reliance on statistical tools, another objective of this study is to promote by example the greater use of these tools. To serve this objective, the discussion of the techniques highlights the observed trend toward the use of increasingly more sophisticated methods and points out the strengths and pitfalls of different approaches. Several examples are also given of opportunities for extending EOR research findings by additional statistical manipulation. The search of the EOR literature, conducted mainly through computerized data bases, yielded nearly 200 articles containing mathematical analysis of the research. Of these, 21 were found to include examples of statistical approaches to data analysis and are discussed in detail in this review. The use of statistical techniques, as might be expected from their general purpose nature, extends across nearly all types of EOR research covering thermal methods of recovery, miscible processes, and micellar polymer floods. Data come from field tests, the laboratory, and computer simulation. The statistical methods range from simple comparisons of mean values to multiple non-linear regression equations and to probabilistic decision functions. The methods are applied to both engineering and economic data. The results of the survey are grouped by statistical technique and include brief descriptions of each of the 21 relevant papers. Complete abstracts of the papers are included in the bibliography. Brief bibliographic information (without abstracts) is also given for the articles identified in the initial search as containing mathematical analyses using other than statistical methods.
Statistical Methods Handbook for Advanced Gas Reactor Fuel Materials
J. J. Einerson
2005-05-01
Fuel materials such as kernels, coated particles, and compacts are being manufactured for experiments simulating service in the next generation of high temperature gas reactors. These must meet predefined acceptance specifications. Many tests are performed for quality assurance, and many of these correspond to criteria that must be met with specified confidence, based on random samples. This report describes the statistical methods to be used. The properties of the tests are discussed, including the risk of false acceptance, the risk of false rejection, and the assumption of normality. Methods for calculating sample sizes are also described.
Application of the Bootstrap Statistical Method in Deriving Vibroacoustic Specifications
NASA Technical Reports Server (NTRS)
Hughes, William O.; Paez, Thomas L.
2006-01-01
This paper discusses the Bootstrap Method for specification of vibroacoustic test specifications. Vibroacoustic test specifications are necessary to properly accept or qualify a spacecraft and its components for the expected acoustic, random vibration and shock environments seen on an expendable launch vehicle. Traditionally, NASA and the U.S. Air Force have employed methods of Normal Tolerance Limits to derive these test levels based upon the amount of data available, and the probability and confidence levels desired. The Normal Tolerance Limit method contains inherent assumptions about the distribution of the data. The Bootstrap is a distribution-free statistical subsampling method which uses the measured data themselves to establish estimates of statistical measures of random sources. This is achieved through the computation of large numbers of Bootstrap replicates of a data measure of interest and the use of these replicates to derive test levels consistent with the probability and confidence desired. The comparison of the results of these two methods is illustrated via an example utilizing actual spacecraft vibroacoustic data.
Statistical methods for handling unwanted variation in metabolomics data
Sysi-Aho, Marko; Jacob, Laurent; Gagnon-Bartsch, Johann A.; Castillo, Sandra; Simpson, Julie A; Speed, Terence P.
2015-01-01
Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognised need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely-used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available online. PMID:25692814
Statistical methods for investigating quiescence and other temporal seismicity patterns
Matthews, M.V.; Reasenberg, P.A.
1988-01-01
We propose a statistical model and a technique for objective recognition of one of the most commonly cited seismicity patterns:microearthquake quiescence. We use a Poisson process model for seismicity and define a process with quiescence as one with a particular type of piece-wise constant intensity function. From this model, we derive a statistic for testing stationarity against a 'quiescence' alternative. The large-sample null distribution of this statistic is approximated from simulated distributions of appropriate functionals applied to Brownian bridge processes. We point out the restrictiveness of the particular model we propose and of the quiescence idea in general. The fact that there are many point processes which have neither constant nor quiescent rate functions underscores the need to test for and describe nonuniformity thoroughly. We advocate the use of the quiescence test in conjunction with various other tests for nonuniformity and with graphical methods such as density estimation. ideally these methods may promote accurate description of temporal seismicity distributions and useful characterizations of interesting patterns. ?? 1988 Birkha??user Verlag.
FAst STatistics for weak Lensing (FASTLens): fast method for weak lensing statistics and map making
NASA Astrophysics Data System (ADS)
Pires, S.; Starck, J.-L.; Amara, A.; Teyssier, R.; Réfrégier, A.; Fadili, J.
2009-05-01
With increasingly large data sets, weak lensing measurements are able to measure cosmological parameters with ever-greater precision. However, this increased accuracy also places greater demands on the statistical tools used to extract the available information. To date, the majority of lensing analyses use the two-point statistics of the cosmic shear field. These can be either studied directly using the two-point correlation function or in Fourier space, using the power spectrum. But analysing weak lensing data inevitably involves the masking out of regions, for example to remove bright stars from the field. Masking out the stars is common practice but the gaps in the data need proper handling. In this paper, we show how an inpainting technique allows us to properly fill in these gaps with only NlogN operations, leading to a new image from which we can compute straightforwardly and with a very good accuracy both the power spectrum and the bispectrum. We then propose a new method to compute the bispectrum with a polar FFT algorithm, which has the main advantage of avoiding any interpolation in the Fourier domain. Finally, we propose a new method for dark matter mass map reconstruction from shear observations, which integrates this new inpainting concept. A range of examples based on 3D N-body simulations illustrates the results.
Feasibility of voxel-based statistical analysis method for myocardial PET
NASA Astrophysics Data System (ADS)
Ram Yu, A.; Kim, Jin Su; Paik, Chang H.; Kim, Kyeong Min; Moo Lim, Sang
2014-09-01
Although statistical parametric mapping (SPM) analysis is widely used in neuroimaging studies, to our best knowledge, there was no application to myocardial PET data analysis. In this study, we developed the voxel based statistical analysis method for myocardial PET which provides statistical comparison results between groups in image space. PET Emission data of normal and myocardial infarction rats were acquired For the SPM analysis, a rat heart template was created. In addition, individual PET data was spatially normalized and smoothed. Two sample t-tests were performed to identify the myocardial infarct region. This developed SPM method was compared with conventional ROI methods. Myocardial glucose metabolism was decreased in the lateral wall of the left ventricle. In the result of ROI analysis, the mean value of the lateral wall was 29% decreased. The newly developed SPM method for myocardial PET could provide quantitative information in myocardial PET study.
Application of rational functions for the standard addition method.
Gorazda, Katarzyna; Michałowska-Kaczmarczyk, Anna M; Asuero, Agustin G; Michałowski, Tadeusz
2013-11-15
Some rational functions are considered as the basis for calculation of unknown concentration (x0) of an analyte X determined according to the standard addition method (SAM). The correction for dilution of the sample tested during addition of successive increments of standard(ised) solution of X is involved in the related algorithm applied for calculation of the x0 value. The formulae derived were put in context with experimental data, obtained according to the AAS method from Cu-measurements in samples obtained by digestion of an ash obtained from incinerated sludge. It was stated that the use of rational functions for modeling purposes strengthens the robustness of the results thus obtained. PMID:24148496
Radiological decontamination, survey, and statistical release method for vehicles
Goodwill, M.E.; Lively, J.W.; Morris, R.L.
1996-06-01
Earth-moving vehicles (e.g., dump trucks, belly dumps) commonly haul radiologically contaminated materials from a site being remediated to a disposal site. Traditionally, each vehicle must be surveyed before being released. The logistical difficulties of implementing the traditional approach on a large scale demand that an alternative be devised. A statistical method for assessing product quality from a continuous process was adapted to the vehicle decontamination process. This method produced a sampling scheme that automatically compensates and accommodates fluctuating batch sizes and changing conditions without the need to modify or rectify the sampling scheme in the field. Vehicles are randomly selected (sampled) upon completion of the decontamination process to be surveyed for residual radioactive surface contamination. The frequency of sampling is based on the expected number of vehicles passing through the decontamination process in a given period and the confidence level desired. This process has been successfully used for 1 year at the former uranium millsite in Monticello, Utah (a cleanup site regulated under the Comprehensive Environmental Response, Compensation, and Liability Act). The method forces improvement in the quality of the decontamination process and results in a lower likelihood that vehicles exceeding the surface contamination standards are offered for survey. Implementation of this statistical sampling method on Monticello projects has resulted in more efficient processing of vehicles through decontamination and radiological release, saved hundreds of hours of processing time, provided a high level of confidence that release limits are met, and improved the radiological cleanliness of vehicles leaving the controlled site.
Statistical length measurement method by direct imaging of carbon nanotubes.
Bengio, E Amram; Tsentalovich, Dmitri E; Behabtu, Natnael; Kleinerman, Olga; Kesselman, Ellina; Schmidt, Judith; Talmon, Yeshayahu; Pasquali, Matteo
2014-05-14
The influence of carbon nanotube (CNT) length on their macroscopic properties requires an accurate methodology for CNT length measurement. So far, existing techniques are limited to short (less than a few micrometers) CNTs and sample preparation methods that bias the measured values. Here, we show that the average length of carbon nanotubes (CNTs) can be measured by cryogenic transmission electron microscopy (cryo-TEM) of CNTs in chlorosulfonic acid. The method consists of dissolving at low concentration CNTs in chlorosulfonic acid (a true solvent), imaging the individual CNTs by cryo-TEM, and processing and analyzing the images to determine CNT length. By measuring the total CNT contour length and number of CNT ends in each image, and by applying statistical analysis, we extend the method to cases where each CNT is long enough to span many cryo-TEM images, making the direct length measurement of an entire CNT impractical. Hence, this new technique can be used effectively to estimate samples in a wide range of CNT lengths, although we find that cryo-TEM imaging may bias the measurement towards longer CNTs, which are easier to detect. Our statistical method is also applied to AFM images of CNTs to show that, by using only a few AFM images, it yields estimates that are consistent with literature techniques, based on individually measuring a higher number of CNTs. PMID:24773046
Texture analysis with statistical methods for wheat ear extraction
NASA Astrophysics Data System (ADS)
Bakhouche, M.; Cointault, F.; Gouton, P.
2007-01-01
In agronomic domain, the simplification of crop counting, necessary for yield prediction and agronomic studies, is an important project for technical institutes such as Arvalis. Although the main objective of our global project is to conceive a mobile robot for natural image acquisition directly in a field, Arvalis has proposed us first to detect by image processing the number of wheat ears in images before to count them, which will allow to obtain the first component of the yield. In this paper we compare different texture image segmentation techniques based on feature extraction by first and higher order statistical methods which have been applied on our images. The extracted features are used for unsupervised pixel classification to obtain the different classes in the image. So, the K-means algorithm is implemented before the choice of a threshold to highlight the ears. Three methods have been tested in this feasibility study with very average error of 6%. Although the evaluation of the quality of the detection is visually done, automatic evaluation algorithms are currently implementing. Moreover, other statistical methods of higher order will be implemented in the future jointly with methods based on spatio-frequential transforms and specific filtering.
Decision Making Method Based on Paraconsistent Annotated Logic and Statistical Method: a Comparison
NASA Astrophysics Data System (ADS)
de Carvalho, Fábio Romeu; Brunstein, Israel; Abe, Jair Minoro
2008-10-01
Presently, there are new kinds of logic capable of handling uncertain and contradictory data without becoming trivial. Decision making theories based on these logics have shown to be powerful in many aspects regarding more traditional methods based on Statistics. In this paper we intend to outline a first study for a decision making theory based on Paraconsistent Annotated Evidential Logic Eτ (Paraconsistent Decision Method (PDM)) and classical Statistical Decision Method (SDM). Some discussion is presented below.
Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.
How to eradicate fraudulent statistical methods: statisticians must do science
Bross, I.D. )
1990-12-01
The two steps necessary for the clinical expression of a mutagenic disease, genetic damage and viability, are countervailing forces and therefore the dosage response curve for mutagens must have a maximum. To illustrate that science is common sense reduced to calculation, a new mathematical derivation of this result and supporting data are given. This example also shows that the term 'context-free' is a snare and a delusion. When statistical methods are used in a scientific context where their assumptions are known to fail and where there is a reasonable presumption of intent to deceive, they are fraudulent. Estimation of low-level mutagenic risks by linear extrapolation from high-dose data is one example of such a method that is widely used by Executive Branch agencies. Other examples are given of fraudulent statistical methods that are currently used in biomedical research done by or for U.S. government agencies. In the long run, it is argued, the surest way to eradicate such fraud is for biostatisticians to do their own science.
SHUFFLE: A New Statistical Bootstrap Method: Applied to Cosmological Filaments
NASA Astrophysics Data System (ADS)
Bhavsar, Suketu P.; Bharadwaj, Somnath; Sheth, Jatush V.
2003-05-01
We introduce Shuffle, a powerful statistical procedure devised by Bhavsar and Ling [1] to determine the true physical extent of the filaments in the Las Campanas Redshift Survey [LCRS]. At its heart, Shuffle falls in the category of bootstrap like methods [2]. We find that the longest physical filamentary structures in 5 of the 6 LCRS slices are longer than 50 h-1 Mpc but not quite extending to 70 h-1 Mpc. The -3 degree slice contains filamentary structure longer than 70 h-1 Mpc.
Of pacemakers and statistics: the actuarial method extended.
Dussel, J; Wolbarst, A B; Scott-Millar, R N; Obel, I W
1980-01-01
Pacemakers cease functioning because of either natural battery exhaustion (nbe) or component failure (cf). A study of four series of pacemakers shows that a simple extension of the actuarial method, so as to incorporate Normal statistics, makes possible a quantitative differentiation between the two modes of failure. This involves the separation of the overall failure probability density function PDF(t) into constituent parts pdfnbe(t) and pdfcf(t). The approach should allow a meaningful comparison of the characteristics of different pacemaker types. PMID:6160497
Statistical estimation of mineral age by K-Ar method
Vistelius, A.B.; Drubetzkoy, E.R.; Faas, A.V. )
1989-11-01
Statistical estimation of age of {sup 40}Ar/{sup 40}K ratios may be considered a result of convolution of uniform and normal distributions with different weights for different minerals. Data from Gul'shad Massif (Nearbalkhash, Kazakhstan, USSR) indicate that {sup 40}Ar/{sup 40}K ratios reflecting the intensity of geochemical processes can be resolved using convolutions. Loss of {sup 40}Ar in biotites is shown whereas hornblende retained the original content of {sup 40}Ar throughout the geological history of the massif. Results demonstrate that different estimation methods must be used for different minerals and different rocks when radiometric ages are employed for dating.
Estimated Accuracy of Three Common Trajectory Statistical Methods
NASA Technical Reports Server (NTRS)
Kabashnikov, Vitaliy P.; Chaikovsky, Anatoli P.; Kucsera, Tom L.; Metelskaya, Natalia S.
2011-01-01
Three well-known trajectory statistical methods (TSMs), namely concentration field (CF), concentration weighted trajectory (CWT), and potential source contribution function (PSCF) methods were tested using known sources and artificially generated data sets to determine the ability of TSMs to reproduce spatial distribution of the sources. In the works by other authors, the accuracy of the trajectory statistical methods was estimated for particular species and at specified receptor locations. We have obtained a more general statistical estimation of the accuracy of source reconstruction and have found optimum conditions to reconstruct source distributions of atmospheric trace substances. Only virtual pollutants of the primary type were considered. In real world experiments, TSMs are intended for application to a priori unknown sources. Therefore, the accuracy of TSMs has to be tested with all possible spatial distributions of sources. An ensemble of geographical distributions of virtual sources was generated. Spearman s rank order correlation coefficient between spatial distributions of the known virtual and the reconstructed sources was taken to be a quantitative measure of the accuracy. Statistical estimates of the mean correlation coefficient and a range of the most probable values of correlation coefficients were obtained. All the TSMs that were considered here showed similar close results. The maximum of the ratio of the mean correlation to the width of the correlation interval containing the most probable correlation values determines the optimum conditions for reconstruction. An optimal geographical domain roughly coincides with the area supplying most of the substance to the receptor. The optimal domain s size is dependent on the substance decay time. Under optimum reconstruction conditions, the mean correlation coefficients can reach 0.70 0.75. The boundaries of the interval with the most probable correlation values are 0.6 0.9 for the decay time of 240 h
Statistical Methods for Linking Health, Exposure, and Hazards
Mather, Frances Jean; White, LuAnn Ellis; Langlois, Elizabeth Cullen; Shorter, Charles Franklin; Swalm, Christopher Martin; Shaffer, Jeffrey George; Hartley, William Ralph
2004-01-01
The Environmental Public Health Tracking Network (EPHTN) proposes to link environmental hazards and exposures to health outcomes. Statistical methods used in case–control and cohort studies to link health outcomes to individual exposure estimates are well developed. However, reliable exposure estimates for many contaminants are not available at the individual level. In these cases, exposure/hazard data are often aggregated over a geographic area, and ecologic models are used to relate health outcome and exposure/hazard. Ecologic models are not without limitations in interpretation. EPHTN data are characteristic of much information currently being collected—they are multivariate, with many predictors and response variables, often aggregated over geographic regions (small and large) and correlated in space and/or time. The methods to model trends in space and time, handle correlation structures in the data, estimate effects, test hypotheses, and predict future outcomes are relatively new and without extensive application in environmental public health. In this article we outline a tiered approach to data analysis for EPHTN and review the use of standard methods for relating exposure/hazards, disease mapping and clustering techniques, Bayesian approaches, Markov chain Monte Carlo methods for estimation of posterior parameters, and geostatistical methods. The advantages and limitations of these methods are discussed. PMID:15471740
A Statistical Process Control Method for Semiconductor Manufacturing
NASA Astrophysics Data System (ADS)
Kubo, Tomoaki; Ino, Tomomi; Minami, Kazuhiro; Minami, Masateru; Homma, Tetsuya
To maintain stable operation of semiconductor fabrication lines, statistical process control (SPC) methods are recognized to be effective. However, in semiconductor fabrication lines, there exist a huge number of process state signals to be monitored, and these signals contain both normally and non-normally distributed data. Therefore, if we try to apply SPC methods to those signals, we need one which satisfies three requirements: 1) It can deal with both normally distributed data, and non-normally distributed data, 2) It can be set up automatically, 3) It can be easily understood by engineers and technicians. In this paper, we propose a new SPC method which satisfies these three requirements at the same time. This method uses similar rules to the Shewhart chart, but can deal with non-normally distributed data by introducing “effective standard deviations”. Usefulness of this method is demonstrated by comparing false alarm ratios to that of the Shewhart chart method. In the demonstration, we use various kinds of artificially generated data, and real data observed in a chemical vapor deposition (CVD) process tool in a semiconductor fabrication line.
FOREWORD: Special issue on Statistical and Probabilistic Methods for Metrology
NASA Astrophysics Data System (ADS)
Bich, Walter; Cox, Maurice G.
2006-08-01
This special issue of Metrologia is the first that is not devoted to units, or constants, or measurement techniques in some specific field of metrology, but to the generic topic of statistical and probabilistic methods for metrology. The number of papers on this subject in measurement journals, and in Metrologia in particular, has continued to increase over the years, driven by the publication of the Guide to the Expression of Uncertainty in Measurement (GUM) [1] and the Mutual Recognition Arrangement (MRA) of the CIPM [2]. The former stimulated metrologists to think in greater depth about the appropriate modelling of their measurements, in order to provide uncertainty evaluations associated with measurement results. The latter obliged the metrological community to investigate reliable measures for assessing the calibration and measurement capabilities declared by the national metrology institutes (NMIs). Furthermore, statistical analysis of measurement data became even more important than hitherto, with the need, on the one hand, to treat the greater quantities of data provided by sophisticated measurement systems, and, on the other, to deal appropriately with relatively small sets of data that are difficult or expensive to obtain. The importance of supporting the GUM and extending its provisions was recognized by the formation in the year 2000 of Working Group 1, Measurement uncertainty, of the Joint Committee for Guides in Metrology. The need to provide guidance on key comparison data evaluation was recognized by the formation in the year 2001 of the BIPM Director's Advisory Group on Uncertainty. A further international initiative was the revision, in the year 2004, of the remit and title of a working group of ISO/TC 69, Application of Statistical Methods, to reflect the need to concentrate more on statistical methods to support measurement uncertainty evaluation. These international activities are supplemented by national programmes such as the Software Support
New statistical method for machine-printed Arabic character recognition
NASA Astrophysics Data System (ADS)
Wang, Hua; Ding, Xiaoqing; Jin, Jianming; Halmurat, M.
2004-12-01
Although about 300 million people worldwide, in several different languages, take Arabic characters for writing, Arabic OCR has not been researched as thoroughly as other widely used characters (Latin or Chinese). In this paper, a new statistical method is developed to recognize machine-printed Arabic characters. Firstly, the entire Arabic character set is pre-classified into 32 sub-sets in terms of character forms, special zones that characters occupy and component information. Then directional features are extracted based on which modified quadratic discriminant function (MQDF) is utilized as classifier to deal with classification task. Finally, similar characters are discriminated before outputting recognition results. Encouraging experimental results on test sets show the validity of proposed method.
New statistical method for machine-printed Arabic character recognition
NASA Astrophysics Data System (ADS)
Wang, Hua; Ding, Xiaoqing; Jin, Jianming; Halmurat, M.
2005-01-01
Although about 300 million people worldwide, in several different languages, take Arabic characters for writing, Arabic OCR has not been researched as thoroughly as other widely used characters (Latin or Chinese). In this paper, a new statistical method is developed to recognize machine-printed Arabic characters. Firstly, the entire Arabic character set is pre-classified into 32 sub-sets in terms of character forms, special zones that characters occupy and component information. Then directional features are extracted based on which modified quadratic discriminant function (MQDF) is utilized as classifier to deal with classification task. Finally, similar characters are discriminated before outputting recognition results. Encouraging experimental results on test sets show the validity of proposed method.
Regional homogenization of surface temperature records using robust statistical methods
NASA Astrophysics Data System (ADS)
Pintar, A. L.; Possolo, A.; Zhang, N. F.
2013-09-01
An algorithm is described that is intended to estimate and remove spurious influences from the surface temperature record at a meteorological station, which may be due to changes in the location of the station or in its environment, or in the method used to make measurements, and which are unrelated to climate change, similarly to [1]. The estimate of these influences is based on a comparison of non-parametric decompositions of the target series with series measured at other stations in a neighborhood of the target series. The uncertainty of the estimated spurious artifacts is determined using a statistical bootstrap method that accounts for temporal correlation structure beyond what is expected from seasonal effects. Our computer-intensive bootstrap procedure lends itself readily to parallelization, which makes the algorithm practicable for large collections of stations. The role that the proposed procedure may play in practice is contingent on the results of large-scale testing, still under way, using historical data.
Statistical methods for the blood beryllium lymphocyte proliferation test
Frome, E.L.; Smith, M.H.; Littlefield, L.G.
1996-10-01
The blood beryllium lymphocyte proliferation test (BeLPT) is a modification of the standard lymphocyte proliferation test that is used to identify persons who may have chronic beryllium disease. A major problem in the interpretation of BeLPT test results is outlying data values among the replicate well counts ({approx}7%). A log-linear regression model is used to describe the expected well counts for each set of Be exposure conditions, and the variance of the well counts is proportional to the square of the expected count. Two outlier-resistant regression methods are used to estimate stimulation indices (SIs) and the coefficient of variation. The first approach uses least absolute values (LAV) on the log of the well counts as a method for estimation; the second approach uses a resistant regression version of maximum quasi-likelihood estimation. A major advantage of these resistant methods is that they make it unnecessary to identify and delete outliers. These two new methods for the statistical analysis of the BeLPT data and the current outlier rejection method are applied to 173 BeLPT assays. We strongly recommend the LAV method for routine analysis of the BeLPT. Outliers are important when trying to identify individuals with beryllium hypersensitivity, since these individuals typically have large positive SI values. A new method for identifying large SIs using combined data from the nonexposed group and the beryllium workers is proposed. The log(SI)s are described with a Gaussian distribution with location and scale parameters estimated using resistant methods. This approach is applied to the test data and results are compared with those obtained from the current method. 24 refs., 9 figs., 8 tabs.
Statistical method for detecting structural change in the growth process.
Ninomiya, Yoshiyuki; Yoshimoto, Atsushi
2008-03-01
Due to competition among individual trees and other exogenous factors that change the growth environment, each tree grows following its own growth trend with some structural changes in growth over time. In the present article, a new method is proposed to detect a structural change in the growth process. We formulate the method as a simple statistical test for signal detection without constructing any specific model for the structural change. To evaluate the p-value of the test, the tube method is developed because the regular distribution theory is insufficient. Using two sets of tree diameter growth data sampled from planted forest stands of Cryptomeria japonica in Japan, we conduct an analysis of identifying the effect of thinning on the growth process as a structural change. Our results demonstrate that the proposed method is useful to identify the structural change caused by thinning. We also provide the properties of the method in terms of the size and power of the test. PMID:17608782
Measurement of Plethysmogram and Statistical Method for Analysis
NASA Astrophysics Data System (ADS)
Shimizu, Toshihiro
The plethysmogram is measured at different points of human body by using the photo interrupter, which sensitively depends on the physical and mental situation of human body. In this paper the statistical method of the data-analysis is investigated to discuss the dependence of plethysmogram on stress and aging. The first one is the representation method based on the return map, which provides usuful information for the waveform, the flucuation in phase and the fluctuation in amplitude. The return map method makes it possible to understand the fluctuation of plethymogram in amplitude and in phase more clearly and globally than in the conventional power spectrum method. The second is the Lisajous plot and the correlation function to analyze the phase difference between the plethysmograms of the right finger tip and of the left finger tip. The third is the R-index, from which we can estimate “the age of the blood flow”. The R-index is defined by the global character of plethysmogram, which is different from the usual APG-index. The stress- and age-dependence of plethysmogram is discussed by using these methods.
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
Zhao, Ying-Qi; Zeng, Donglin; Laber, Eric B.; Kosorok, Michael R.
2014-01-01
Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamentally different than regression-based methods, for example Q-learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models. We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with Q-learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation. PMID:26236062
On the evolution of statistical methods as applied to clinical trials.
Machin, D
2004-05-01
This paper describes how statistical methods have evolved in parallel with activities associated with randomized control trials. In particular we emphasize the pivotal role of two papers published in British Journal of Cancer, and the paper describing the Cox proportional hazards model. In addition, the importance of early papers on estimating the sample size required for trials is highlighted. Later developments including the increasing roles for competing risks, multilevel modelling and Bayesian methodologies are described. The interplay between computer software and statistical methodological developments is stressed. Finally some future directions are indicated. PMID:15078495
Jet Noise Diagnostics Supporting Statistical Noise Prediction Methods
NASA Technical Reports Server (NTRS)
Bridges, James E.
2006-01-01
compared against measurements of mean and rms velocity statistics over a range of jet speeds and temperatures. Models for flow parameters used in the acoustic analogy, most notably the space-time correlations of velocity, have been compared against direct measurements, and modified to better fit the observed data. These measurements have been extremely challenging for hot, high speed jets, and represent a sizeable investment in instrumentation development. As an intermediate check that the analysis is predicting the physics intended, phased arrays have been employed to measure source distributions for a wide range of jet cases. And finally, careful far-field spectral directivity measurements have been taken for final validation of the prediction code. Examples of each of these experimental efforts will be presented. The main result of these efforts is a noise prediction code, named JeNo, which is in middevelopment. JeNo is able to consistently predict spectral directivity, including aft angle directivity, for subsonic cold jets of most geometries. Current development on JeNo is focused on extending its capability to hot jets, requiring inclusion of a previously neglected second source associated with thermal fluctuations. A secondary result of the intensive experimentation is the archiving of various flow statistics applicable to other acoustic analogies and to development of time-resolved prediction methods. These will be of lasting value as we look ahead at future challenges to the aeroacoustic experimentalist.
Data and statistical methods for analysis of trends and patterns
Atwood, C.L.; Gentillon, C.D.; Wilson, G.E.
1992-11-01
This report summarizes topics considered at a working meeting on data and statistical methods for analysis of trends and patterns in US commercial nuclear power plants. This meeting was sponsored by the Office of Analysis and Evaluation of Operational Data (AEOD) of the Nuclear Regulatory Commission (NRC). Three data sets are briefly described: Nuclear Plant Reliability Data System (NPRDS), Licensee Event Report (LER) data, and Performance Indicator data. Two types of study are emphasized: screening studies, to see if any trends or patterns appear to be present; and detailed studies, which are more concerned with checking the analysis assumptions, modeling any patterns that are present, and searching for causes. A prescription is given for a screening study, and ideas are suggested for a detailed study, when the data take of any of three forms: counts of events per time, counts of events per demand, and non-event data.
Statistical Methods for Rapid Aerothermal Analysis and Design Technology: Validation
NASA Technical Reports Server (NTRS)
DePriest, Douglas; Morgan, Carolyn
2003-01-01
The cost and safety goals for NASA s next generation of reusable launch vehicle (RLV) will require that rapid high-fidelity aerothermodynamic design tools be used early in the design cycle. To meet these requirements, it is desirable to identify adequate statistical models that quantify and improve the accuracy, extend the applicability, and enable combined analyses using existing prediction tools. The initial research work focused on establishing suitable candidate models for these purposes. The second phase is focused on assessing the performance of these models to accurately predict the heat rate for a given candidate data set. This validation work compared models and methods that may be useful in predicting the heat rate.
A Comparison of Three Presentation Methods of Teaching Statistics.
ERIC Educational Resources Information Center
Packard, Abbot L.; And Others
The use of computer assisted instruction in teaching statistical concepts was studied. Students enrolled in classes in education who lacked statistical experience participated. Knowledge questions for pretest and posttest assessments were prepared from a pool of questions used in the statistics department of the College of Education at Virginia…
Systematic variational method for statistical nonlinear state and parameter estimation
NASA Astrophysics Data System (ADS)
Ye, Jingxin; Rey, Daniel; Kadakia, Nirag; Eldridge, Michael; Morone, Uriel I.; Rozdeba, Paul; Abarbanel, Henry D. I.; Quinn, John C.
2015-11-01
In statistical data assimilation one evaluates the conditional expected values, conditioned on measurements, of interesting quantities on the path of a model through observation and prediction windows. This often requires working with very high dimensional integrals in the discrete time descriptions of the observations and model dynamics, which become functional integrals in the continuous-time limit. Two familiar methods for performing these integrals include (1) Monte Carlo calculations and (2) variational approximations using the method of Laplace plus perturbative corrections to the dominant contributions. We attend here to aspects of the Laplace approximation and develop an annealing method for locating the variational path satisfying the Euler-Lagrange equations that comprises the major contribution to the integrals. This begins with the identification of the minimum action path starting with a situation where the model dynamics is totally unresolved in state space, and the consistent minimum of the variational problem is known. We then proceed to slowly increase the model resolution, seeking to remain in the basin of the minimum action path, until a path that gives the dominant contribution to the integral is identified. After a discussion of some general issues, we give examples of the assimilation process for some simple, instructive models from the geophysical literature. Then we explore a slightly richer model of the same type with two distinct time scales. This is followed by a model characterizing the biophysics of individual neurons.
Statistically qualified neuro-analytic failure detection method and system
Vilim, Richard B.; Garcia, Humberto E.; Chen, Frederick W.
2002-03-02
An apparatus and method for monitoring a process involve development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two stages: deterministic model adaption and stochastic model modification of the deterministic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics, augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation error minimization technique. Stochastic model modification involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system. Illustrative of the method and apparatus, the method is applied to a peristaltic pump system.
Systematic variational method for statistical nonlinear state and parameter estimation.
Ye, Jingxin; Rey, Daniel; Kadakia, Nirag; Eldridge, Michael; Morone, Uriel I; Rozdeba, Paul; Abarbanel, Henry D I; Quinn, John C
2015-11-01
In statistical data assimilation one evaluates the conditional expected values, conditioned on measurements, of interesting quantities on the path of a model through observation and prediction windows. This often requires working with very high dimensional integrals in the discrete time descriptions of the observations and model dynamics, which become functional integrals in the continuous-time limit. Two familiar methods for performing these integrals include (1) Monte Carlo calculations and (2) variational approximations using the method of Laplace plus perturbative corrections to the dominant contributions. We attend here to aspects of the Laplace approximation and develop an annealing method for locating the variational path satisfying the Euler-Lagrange equations that comprises the major contribution to the integrals. This begins with the identification of the minimum action path starting with a situation where the model dynamics is totally unresolved in state space, and the consistent minimum of the variational problem is known. We then proceed to slowly increase the model resolution, seeking to remain in the basin of the minimum action path, until a path that gives the dominant contribution to the integral is identified. After a discussion of some general issues, we give examples of the assimilation process for some simple, instructive models from the geophysical literature. Then we explore a slightly richer model of the same type with two distinct time scales. This is followed by a model characterizing the biophysics of individual neurons. PMID:26651756
Aversi-Ferreira, Roqueline A. G. M. F.; Nishijo, Hisao; Aversi-Ferreira, Tales Alexandre
2015-01-01
Various statistical methods have been published for comparative anatomy. However, few studies compared parametric and nonparametric statistical methods. Moreover, some previous studies using statistical method for comparative anatomy (SMCA) proposed the formula for comparison of groups of anatomical structures (multiple structures) among different species. The present paper described the usage of SMCA and compared the results by SMCA with those by parametric test (t-test) and nonparametric analyses (cladistics) of anatomical data. In conclusion, the SMCA can offer a more exact and precise way to compare single and multiple anatomical structures across different species, which requires analyses of nominal features in comparative anatomy. PMID:26413553
Aversi-Ferreira, Roqueline A G M F; Nishijo, Hisao; Aversi-Ferreira, Tales Alexandre
2015-01-01
Various statistical methods have been published for comparative anatomy. However, few studies compared parametric and nonparametric statistical methods. Moreover, some previous studies using statistical method for comparative anatomy (SMCA) proposed the formula for comparison of groups of anatomical structures (multiple structures) among different species. The present paper described the usage of SMCA and compared the results by SMCA with those by parametric test (t-test) and nonparametric analyses (cladistics) of anatomical data. In conclusion, the SMCA can offer a more exact and precise way to compare single and multiple anatomical structures across different species, which requires analyses of nominal features in comparative anatomy. PMID:26413553
Emperical Laws in Economics Uncovered Using Methods in Statistical Mechanics
NASA Astrophysics Data System (ADS)
Stanley, H. Eugene
2001-06-01
In recent years, statistical physicists and computational physicists have determined that physical systems which consist of a large number of interacting particles obey universal "scaling laws" that serve to demonstrate an intrinsic self-similarity operating in such systems. Further, the parameters appearing in these scaling laws appear to be largely independent of the microscopic details. Since economic systems also consist of a large number of interacting units, it is plausible that scaling theory can be usefully applied to economics. To test this possibility using realistic data sets, a number of scientists have begun analyzing economic data using methods of statistical physics [1]. We have found evidence for scaling (and data collapse), as well as universality, in various quantities, and these recent results will be reviewed in this talk--starting with the most recent study [2]. We also propose models that may lead to some insight into these phenomena. These results will be discussed, as well as the overall rationale for why one might expect scaling principles to hold for complex economic systems. This work on which this talk is based is supported by BP, and was carried out in collaboration with L. A. N. Amaral S. V. Buldyrev, D. Canning, P. Cizeau, X. Gabaix, P. Gopikrishnan, S. Havlin, Y. Lee, Y. Liu, R. N. Mantegna, K. Matia, M. Meyer, C.-K. Peng, V. Plerou, M. A. Salinger, and M. H. R. Stanley. [1.] See, e.g., R. N. Mantegna and H. E. Stanley, Introduction to Econophysics: Correlations & Complexity in Finance (Cambridge University Press, Cambridge, 1999). [2.] P. Gopikrishnan, B. Rosenow, V. Plerou, and H. E. Stanley, "Identifying Business Sectors from Stock Price Fluctuations," e-print cond-mat/0011145; V. Plerou, P. Gopikrishnan, L. A. N. Amaral, X. Gabaix, and H. E. Stanley, "Diffusion and Economic Fluctuations," Phys. Rev. E (Rapid Communications) 62, 3023-3026 (2000); P. Gopikrishnan, V. Plerou, X. Gabaix, and H. E. Stanley, "Statistical Properties of
Wallace, D L; Perlman, M D
1980-06-01
This report describes the research activities of the Department of Statistics, University of Chicago, during the period June 15, 1975 to July 30, 1979. Nine research projects are briefly described on the following subjects: statistical computing and approximation techniques in statistics; numerical computation of first passage distributions; probabilities of large deviations; combining independent tests of significance; small-sample efficiencies of tests and estimates; improved procedures for simultaneous estimation and testing of many correlations; statistical computing and improved regression methods; comparison of several populations; and unbiasedness in multivariate statistics. A description of the statistical consultation activities of the Department that are of interest to DOE, in particular, the scientific interactions between the Department and the scientists at Argonne National Laboratories, is given. A list of publications issued during the term of the contract is included.
Statistical methods for texture analysis applied to agronomical images
NASA Astrophysics Data System (ADS)
Cointault, F.; Journaux, L.; Gouton, P.
2008-02-01
For activities of agronomical research institute, the land experimentations are essential and provide relevant information on crops such as disease rate, yield components, weed rate... Generally accurate, they are manually done and present numerous drawbacks, such as penibility, notably for wheat ear counting. In this case, the use of color and/or texture image processing to estimate the number of ears per square metre can be an improvement. Then, different image segmentation techniques based on feature extraction have been tested using textural information with first and higher order statistical methods. The Run Length method gives the best results closed to manual countings with an average error of 3%. Nevertheless, a fine justification of hypothesis made on the values of the classification and description parameters is necessary, especially for the number of classes and the size of analysis windows, through the estimation of a cluster validity index. The first results show that the mean number of classes in wheat image is of 11, which proves that our choice of 3 is not well adapted. To complete these results, we are currently analysing each of the class previously extracted to gather together all the classes characterizing the ears.
System Synthesis in Preliminary Aircraft Design using Statistical Methods
NASA Technical Reports Server (NTRS)
DeLaurentis, Daniel; Mavris, Dimitri N.; Schrage, Daniel P.
1996-01-01
This paper documents an approach to conceptual and preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically design of experiments (DOE) and response surface methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an overall evaluation criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in a innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting a solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a high speed civil transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabalistic designs (and eventually robust ones).
System Synthesis in Preliminary Aircraft Design Using Statistical Methods
NASA Technical Reports Server (NTRS)
DeLaurentis, Daniel; Mavris, Dimitri N.; Schrage, Daniel P.
1996-01-01
This paper documents an approach to conceptual and early preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically Design of Experiments (DOE) and Response Surface Methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an Overall Evaluation Criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in an innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting in solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a High Speed Civil Transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabilistic designs (and eventually robust ones).
Tips and Tricks for Successful Application of Statistical Methods to Biological Data.
Schlenker, Evelyn
2016-01-01
This chapter discusses experimental design and use of statistics to describe characteristics of data (descriptive statistics) and inferential statistics that test the hypothesis posed by the investigator. Inferential statistics, based on probability distributions, depend upon the type and distribution of the data. For data that are continuous, randomly and independently selected, as well as normally distributed more powerful parametric tests such as Student's t test and analysis of variance (ANOVA) can be used. For non-normally distributed or skewed data, transformation of the data (using logarithms) may normalize the data allowing use of parametric tests. Alternatively, with skewed data nonparametric tests can be utilized, some of which rely on data that are ranked prior to statistical analysis. Experimental designs and analyses need to balance between committing type 1 errors (false positives) and type 2 errors (false negatives). For a variety of clinical studies that determine risk or benefit, relative risk ratios (random clinical trials and cohort studies) or odds ratios (case-control studies) are utilized. Although both use 2 × 2 tables, their premise and calculations differ. Finally, special statistical methods are applied to microarray and proteomics data, since the large number of genes or proteins evaluated increase the likelihood of false discoveries. Additional studies in separate samples are used to verify microarray and proteomic data. Examples in this chapter and references are available to help continued investigation of experimental designs and appropriate data analysis. PMID:26585142
NASA Astrophysics Data System (ADS)
Plotnikov, M. Yu.; Shkarupa, E. V.
2015-11-01
Presently, the direct simulation Monte Carlo (DSMC) method is widely used for solving rarefied gas dynamics problems. As applied to steady-state problems, a feature of this method is the use of dependent sample values of random variables for the calculation of macroparameters of gas flows. A new combined approach to estimating the statistical error of the method is proposed that does not practically require additional computations, and it is applicable for any degree of probabilistic dependence of sample values. Features of the proposed approach are analyzed theoretically and numerically. The approach is tested using the classical Fourier problem and the problem of supersonic flow of rarefied gas through permeable obstacle.
Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods
NASA Astrophysics Data System (ADS)
Werner, A. T.; Cannon, A. J.
2015-06-01
Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event
Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods
NASA Astrophysics Data System (ADS)
Werner, Arelia T.; Cannon, Alex J.
2016-04-01
Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event
Method for controlling a laser additive process using intrinsic illumination
NASA Astrophysics Data System (ADS)
Tait, Robert; Cai, Guoshuang; Azer, Magdi; Chen, Xiaobin; Liu, Yong; Harding, Kevin
2015-05-01
One form of additive manufacturing is to use a laser to generate a melt pool from powdered metal that is sprayed from a nozzle. The laser net-shape machining system builds the part a layer at a time by following a predetermined path. However, because the path may need to take many turns, maintaining a constant melt pool may not be easy. A straight section may require one speed and power while a sharp bend would over melt the metal at the same settings. This paper describes a process monitoring method that uses the intrinsic IR radiation from the melt pool along with a process model configured to establish target values for the parameters associated with the manufacture or repair. This model is based upon known properties of the metal being used as well as the properties of the laser beam. An adaptive control technique is then employed to control process parameters of the machining system based upon the real-time weld pool measurement. Since the system uses the heat radiant from the melt pool, other previously deposited metal does not confuse the system as only the melted material is seen by the camera.
Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs
Tang, Zheng-Zheng; Lin, Dan-Yu
2015-01-01
There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations—MASS, RAREMETAL, MetaSKAT, and seqMeta—in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics. PMID:26094574
Equivalence Class Formation: A Method for Teaching Statistical Interactions
ERIC Educational Resources Information Center
Fields, Lanny; Travis, Robert; Roy, Deborah; Yadlovker, Eytan; de Aguiar-Rocha, Liliane; Sturmey, Peter
2009-01-01
Many students struggle with statistical concepts such as interaction. In an experimental group, participants took a paper-and-pencil test and then were given training to establish equivalent classes containing four different statistical interactions. All participants formed the equivalence classes and showed maintenance when probes contained novel…
A Statistical Method for Quantifying Songbird Phonology and Syntax
Wu, Wei; Thompson, John A.; Bertram, Richard; Johnson, Frank
2008-01-01
Songbirds are the preeminent animal model for understanding how the brain encodes and produces learned vocalizations. Here, we report a new statistical method, the Kullback-Leibler (K-L) distance, for analyzing vocal change over time. First, we use a computerized recording system to capture all song syllables produced by birds each day. Sound Analysis Pro software (Tchernichovski et al., 2000) is then used to measure the duration of each syllable as well as four spectral features: pitch, entropy, frequency modulation, and pitch goodness. Next, 2-dimensional scatter plots of each day of singing are created where syllable duration is on the x-axis and each of the spectral features is represented separately on the y-axis. Each point in the scatter plots represents one syllable and we regard these plots as random samples from a probability distribution. We then apply the standard information-theoretic quantity K-L distance to measure dissimilarity in phonology across days of singing. A variant of this procedure can also be used to analyze differences in syllable syntax. PMID:18674560
A statistical method for draft tube pressure pulsation analysis
NASA Astrophysics Data System (ADS)
Doerfler, P. K.; Ruchonnet, N.
2012-11-01
Draft tube pressure pulsation (DTPP) in Francis turbines is composed of various components originating from different physical phenomena. These components may be separated because they differ by their spatial relationships and by their propagation mechanism. The first step for such an analysis was to distinguish between so-called synchronous and asynchronous pulsations; only approximately periodic phenomena could be described in this manner. However, less regular pulsations are always present, and these become important when turbines have to operate in the far off-design range, in particular at very low load. The statistical method described here permits to separate the stochastic (random) component from the two traditional 'regular' components. It works in connection with the standard technique of model testing with several pressure signals measured in draft tube cone. The difference between the individual signals and the averaged pressure signal, together with the coherence between the individual pressure signals is used for analysis. An example reveals that a generalized, non-periodic version of the asynchronous pulsation is important at low load.
NASA Astrophysics Data System (ADS)
Ghannadpour, Seyyed Saeed; Hezarkhani, Ardeshir
2016-03-01
The U-statistic method is one of the most important structural methods to separate the anomaly from the background. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and determine anomalous areas. In the present study, to use U-statistic method in three-dimensional (3D) condition, U-statistic is applied on the grade of two ideal test examples, by considering sample Z values (elevation). So far, this is the first time that this method has been applied on a 3D condition. To evaluate the performance of 3D U-statistic method and in order to compare U-statistic with one non-structural method, the method of threshold assessment based on median and standard deviation (MSD method) is applied on the two example tests. Results show that the samples indicated by U-statistic method as anomalous are more regular and involve less dispersion than those indicated by the MSD method. So that, according to the location of anomalous samples, denser areas of them can be determined as promising zones. Moreover, results show that at a threshold of U = 0, the total error of misclassification for U-statistic method is much smaller than the total error of criteria of bar {x}+n× s. Finally, 3D model of two test examples for separating anomaly from background using 3D U-statistic method is provided. The source code for a software program, which was developed in the MATLAB programming language in order to perform the calculations of the 3D U-spatial statistic method, is additionally provided. This software is compatible with all the geochemical varieties and can be used in similar exploration projects.
ERIC Educational Resources Information Center
Barron, Kenneth E.; Apple, Kevin J.
2014-01-01
Coursework in statistics and research methods is a core requirement in most undergraduate psychology programs. However, is there an optimal way to structure and sequence methodology courses to facilitate student learning? For example, should statistics be required before research methods, should research methods be required before statistics, or…
Statistical Models and Methods for Network Meta-Analysis.
Madden, L V; Piepho, H-P; Paul, P A
2016-08-01
Meta-analysis, the methodology for analyzing the results from multiple independent studies, has grown tremendously in popularity over the last four decades. Although most meta-analyses involve a single effect size (summary result, such as a treatment difference) from each study, there are often multiple treatments of interest across the network of studies in the analysis. Multi-treatment (or network) meta-analysis can be used for simultaneously analyzing the results from all the treatments. However, the methodology is considerably more complicated than for the analysis of a single effect size, and there have not been adequate explanations of the approach for agricultural investigations. We review the methods and models for conducting a network meta-analysis based on frequentist statistical principles, and demonstrate the procedures using a published multi-treatment plant pathology data set. A major advantage of network meta-analysis is that correlations of estimated treatment effects are automatically taken into account when an appropriate model is used. Moreover, treatment comparisons may be possible in a network meta-analysis that are not possible in a single study because all treatments of interest may not be included in any given study. We review several models that consider the study effect as either fixed or random, and show how to interpret model-fitting output. We further show how to model the effect of moderator variables (study-level characteristics) on treatment effects, and present one approach to test for the consistency of treatment effects across the network. Online supplemental files give explanations on fitting the network meta-analytical models using SAS. PMID:27111798
Statistical Signal Processing Methods in Scattering and Imaging
NASA Astrophysics Data System (ADS)
Zambrano Nunez, Maytee
This Ph.D. dissertation project addresses two related topics in wave-based signal processing: 1) Cramer-Rao bound (CRB) analysis of scattering systems formed by pointlike scatterers in one-dimensional (1D) and three-dimensional (3D) spaces. 2) Compressive optical coherent imaging, based on the incorporation of sparsity priors in the reconstructions. The first topic addresses for wave scattering systems in 1D and 3D spaces the information content about scattering parameters, in particular, the targets' positions and strengths, and derived quantities, that is contained in scattering data corresponding to reflective, transmissive, and more general sensing modalities. This part of the dissertation derives the Cramer-Rao bound (CRB) for the estimation of parameters of scalar wave scattering systems formed by point scatterers. The results shed light on the fundamental difference between the approximate Born approximation model for weak scatterers and the more general multiple scattering model, and facilitate the identification of regions in parameter space where multiple scattering facilitates or obstructs the estimation of parameters from scattering data, as well as of sensing configurations giving maximal or minimal information about the parameters. The derived results are illustrated with numerical examples, with particular emphasis on the imaging resolution which we quantify via a relative resolution index borrowed from a previous paper. Additionally, this work investigates fundamental limits of estimation performance for the localization of the targets and the inverse scattering problem. The second topic of the effort describes a novel compressive-sensing-based technique for optical imaging with a coherent single-detector system. This hybrid opto-micro-electromechanical, coherent single-detector imaging system applies the latest developments in the nascent field of compressive sensing to the problem of computational imaging of wavefield intensity from a small number
FASTLens (FAst STatistics for weak Lensing): Fast Method for Weak Lensing Statistics and Map Making
NASA Astrophysics Data System (ADS)
Pires, S.; Starck, J.-L.; Amara, A.; Teyssier, R.; Refregier, A.; Fadili, J.
2010-10-01
The analysis of weak lensing data requires to account for missing data such as masking out of bright stars. To date, the majority of lensing analyses uses the two point-statistics of the cosmic shear field. These can either be studied directly using the two-point correlation function, or in Fourier space, using the power spectrum. The two-point correlation function is unbiased by missing data but its direct calculation will soon become a burden with the exponential growth of astronomical data sets. The power spectrum is fast to estimate but a mask correction should be estimated. Other statistics can be used but these are strongly sensitive to missing data. The solution that is proposed by FASTLens is to properly fill-in the gaps with only NlogN operations, leading to a complete weak lensing mass map from which one can compute straight forwardly and with a very good accuracy any kind of statistics like power spectrum or bispectrum.
1993-02-01
In 1984, 99% of abortions conducted in Bombay, India, were of female fetuses. In 1986-87, 30,000-50,000 female fetuses were aborted in India. In 1987-88, 7 Delhi clinics conducted 13,000 sex determination tests. Thus, discrimination against females begins before birth in India. Some states (Maharashtra, Goa, and Gujarat) have drafted legislation to prevent the use of prenatal diagnostic tests (e.g., ultrasonography) for sex determination purposes. Families make decisions about an infant's nutrition based on the infant's sex so it is not surprising to see a higher incidence of morbidity among girls than boys (e.g., for respiratory infections in 1985, 55.5% vs. 27.3%). Consequently, they are more likely to die than boys. Even though vasectomy is simpler and safer than tubectomy, the government promotes female sterilizations. The percentage of all sexual sterilizations being tubectomy has increased steadily from 84% to 94% (1986-90). Family planning programs focus on female contraceptive methods, despite the higher incidence of adverse health effects from female methods (e.g., IUD causes pain and heavy bleeding). Some women advocates believe the effects to be so great that India should ban contraceptives and injectable contraceptives. The maternal mortality rate is quite high (460/100,000 live births), equaling a lifetime risk of 1:18 of a pregnancy-related death. 70% of these maternal deaths are preventable. Leading causes of maternal deaths in India are anemia, hemorrhage, eclampsia, sepsis, and abortion. Most pregnant women do not receive prenatal care. Untrained personnel attend about 70% of deliveries in rural areas and 29% in urban areas. Appropriate health services and other interventions would prevent the higher age specific death rates for females between 0 and 35 years old. Even though the government does provide maternal and child health services, it needs to stop decreasing resource allocate for health and start increasing it. PMID:12286355
Additives and method for controlling clathrate hydrates in fluid systems
Sloan, E.D. Jr.; Christiansen, R.L.; Lederhos, J.P.; Long, J.P.; Panchalingam, V.; Du, Y.; Sum, A.K.W.
1997-06-17
Discussed is a process for preventing clathrate hydrate masses from detrimentally impeding the possible flow of a fluid susceptible to clathrate hydrate formation. The process is particularly useful in the natural gas and petroleum production, transportation and processing industry where gas hydrate formation can cause serious problems. Additives preferably contain one or more five member, six member and/or seven member cyclic chemical groupings. Additives include polymers having lactam rings. Additives can also contain polyelectrolytes that are believed to improve conformance of polymer additives through steric hindrance and/or charge repulsion. Also, polymers having an amide on which a C{sub 1}-C{sub 4} group is attached to the nitrogen and/or the carbonyl carbon of the amide may be used alone, or in combination with ring-containing polymers for enhanced effectiveness. Polymers having at least some repeating units representative of polymerizing at least one of an oxazoline, an N-substituted acrylamide and an N-vinyl alkyl amide are preferred.
Additives and method for controlling clathrate hydrates in fluid systems
Sloan, Jr., Earle Dendy; Christiansen, Richard Lee; Lederhos, Joseph P.; Long, Jin Ping; Panchalingam, Vaithilingam; Du, Yahe; Sum, Amadeu Kun Wan
1997-01-01
Discussed is a process for preventing clathrate hydrate masses from detrimentally impeding the possible flow of a fluid susceptible to clathrate hydrate formation. The process is particularly useful in the natural gas and petroleum production, transportation and processing industry where gas hydrate formation can cause serious problems. Additives preferably contain one or more five member, six member and/or seven member cyclic chemical groupings. Additives include polymers having lactam rings. Additives can also contain polyelectrolytes that are believed to improve conformance of polymer additives through steric hinderance and/or charge repulsion. Also, polymers having an amide on which a C.sub.1 -C.sub.4 group is attached to the nitrogen and/or the carbonyl carbon of the amide may be used alone, or in combination with ring-containing polymers for enhanced effectiveness. Polymers having at least some repeating units representative of polymerizing at least one of an oxazoline, an N-substituted acrylamide and an N-vinyl alkyl amide are preferred.
Mealing, Nicole; Hayen, Andrew; Newall, Anthony T
2016-06-01
It is important to assess the impact a vaccination programme has on the burden of disease after it is implemented. For example, this may reveal herd immunity effects or vaccine-induced shifts in the incidence of disease or in circulating strains or serotypes of the pathogen. In this article we summarise the key features of infectious diseases that need to be considered when trying to detect any changes in the burden of diseases at a population level as a result of vaccination efforts. We outline the challenges of using routine surveillance databases to monitor infectious diseases, such as the identification of diseased cases and the availability of vaccination status for cases. We highlight the complexities in modelling the underlying patterns in infectious disease rates (e.g. presence of autocorrelation) and discuss the main statistical methods that can be used to control for periodicity (e.g. seasonality) and autocorrelation when assessing the impact of vaccination programmes on burden of disease (e.g. cosinor terms, generalised additive models, autoregressive processes and moving averages). For some analyses, there may be multiple methods that can be used, but it is important for authors to justify the method chosen and discuss any limitations. We present a case study review of the statistical methods used in the literature to assess the rotavirus vaccination programme impact in Australia. The methods used varied and included generalised linear models and descriptive statistics. Not all studies accounted for autocorrelation and seasonality, which can have a major influence on results. We recommend that future analyses consider the strength and weakness of alternative statistical methods and justify their choice. PMID:27156635
Statistical methods of combining information: Applications to sensor data fusion
Burr, T.
1996-12-31
This paper reviews some statistical approaches to combining information from multiple sources. Promising new approaches will be described, and potential applications to combining not-so-different data sources such as sensor data will be discussed. Experiences with one real data set are described.
Basic Statistical Concepts and Methods for Earth Scientists
Olea, Ricardo A.
2008-01-01
INTRODUCTION Statistics is the science of collecting, analyzing, interpreting, modeling, and displaying masses of numerical data primarily for the characterization and understanding of incompletely known systems. Over the years, these objectives have lead to a fair amount of analytical work to achieve, substantiate, and guide descriptions and inferences.
Critical Realism and Statistical Methods--A Response to Nash
ERIC Educational Resources Information Center
Scott, David
2007-01-01
This article offers a defence of critical realism in the face of objections Nash (2005) makes to it in a recent edition of this journal. It is argued that critical and scientific realisms are closely related and that both are opposed to statistical positivism. However, the suggestion is made that scientific realism retains (from statistical…
Accountability Indicators from the Viewpoint of Statistical Method.
ERIC Educational Resources Information Center
Jordan, Larry
Few people seriously regard students as "products" coming off an educational assembly line, but notions about accountability and quality improvement in higher education are pervaded by manufacturing ideas and metaphors. Because numerical indicators of quality are inevitably expressed by trend lines or statistical control chars of some kind, they…
Md Mukarram Hossain, A.S.; Blackburne, Benjamin P.; Shah, Abhijeet; Whelan, Simon
2015-01-01
Evolutionary studies usually use a two-step process to investigate sequence data. Step one estimates a multiple sequence alignment (MSA) and step two applies phylogenetic methods to ask evolutionary questions of that MSA. Modern phylogenetic methods infer evolutionary parameters using maximum likelihood or Bayesian inference, mediated by a probabilistic substitution model that describes sequence change over a tree. The statistical properties of these methods mean that more data directly translates to an increased confidence in downstream results, providing the substitution model is adequate and the MSA is correct. Many studies have investigated the robustness of phylogenetic methods in the presence of substitution model misspecification, but few have examined the statistical properties of those methods when the MSA is unknown. This simulation study examines the statistical properties of the complete two-step process when inferring sequence divergence and the phylogenetic tree topology. Both nucleotide and amino acid analyses are negatively affected by the alignment step, both through inaccurate guide tree estimates and through overfitting to that guide tree. For many alignment tools these effects become more pronounced when additional sequences are added to the analysis. Nucleotide sequences are particularly susceptible, with MSA errors leading to statistical support for long-branch attraction artifacts, which are usually associated with gross substitution model misspecification. Amino acid MSAs are more robust, but do tend to arbitrarily resolve multifurcations in favor of the guide tree. No inference strategies produce consistently accurate estimates of divergence between sequences, although amino acid MSAs are again more accurate than their nucleotide counterparts. We conclude with some practical suggestions about how to limit the effect of MSA uncertainty on evolutionary inference. PMID:26139831
System and method for high power diode based additive manufacturing
El-Dasher, Bassem S.; Bayramian, Andrew; Demuth, James A.; Farmer, Joseph C.; Torres, Sharon G.
2016-04-12
A system is disclosed for performing an Additive Manufacturing (AM) fabrication process on a powdered material forming a substrate. The system may make use of a diode array for generating an optical signal sufficient to melt a powdered material of the substrate. A mask may be used for preventing a first predetermined portion of the optical signal from reaching the substrate, while allowing a second predetermined portion to reach the substrate. At least one processor may be used for controlling an output of the diode array.
NASA Technical Reports Server (NTRS)
Firstenberg, H.
1971-01-01
The statistics are considered of the Monte Carlo method relative to the interpretation of the NUGAM2 and NUGAM3 computer code results. A numerical experiment using the NUGAM2 code is presented and the results are statistically interpreted.
Statistical Methods for Rapid Aerothermal Analysis and Design Technology
NASA Technical Reports Server (NTRS)
Morgan, Carolyn; DePriest, Douglas; Thompson, Richard (Technical Monitor)
2002-01-01
The cost and safety goals for NASA's next generation of reusable launch vehicle (RLV) will require that rapid high-fidelity aerothermodynamic design tools be used early in the design cycle. To meet these requirements, it is desirable to establish statistical models that quantify and improve the accuracy, extend the applicability, and enable combined analyses using existing prediction tools. The research work was focused on establishing the suitable mathematical/statistical models for these purposes. It is anticipated that the resulting models can be incorporated into a software tool to provide rapid, variable-fidelity, aerothermal environments to predict heating along an arbitrary trajectory. This work will support development of an integrated design tool to perform automated thermal protection system (TPS) sizing and material selection.
Deep Mixing in Stellar Variability: Improved Method, Statistics, and Applications
NASA Astrophysics Data System (ADS)
Arkhypov, Oleksiy V.; Khodachenko, Maxim L.; Lammer, Helmut; Güdel, Manuel; Lüftinger, Theresa; Johnstone, Colin P.
2016-07-01
The preliminary results on deep-mixing manifestations in stellar variability are tested using our improved method and extended data set. We measure the timescales τ m of the stochastic change in the spectral power of rotational harmonics with numbers m ≤ 3 in the light curves of 1361 main-sequence stars from the Kepler mission archive. We find that the gradient [{log}({τ }2)-{log}({τ }1)]/[{log}(2)-{log}(1)] has a histogram maximum at ‑2/3, demonstrating agreement with Kolmogorov’s theory of turbulence and therefore confirming the manifestation of deep mixing. The squared amplitudes of the first and second rotational harmonics, corrected for integral photometry distortion, also show a quasi-Kolmogorov character with spectral index ≈‑5/3. Moreover, the reduction of τ 1 and τ 2 to the timescales τ lam1 and τ lam2 of laminar convection in the deep stellar layers reveals the proximity of both τ lam1 and τ lam2 to the turnover time τ MLT of standard mixing length theory. Considering this result, we use the obtained stellar variability timescales instead of τ MLT in our analysis of the relation between stellar activity and the Rossby number P/τ MLT. Comparison of our diagrams with previous results and theoretical expectations shows that best-fit correspondence is achieved for τ lam1, which can therefore be used as an analog of τ MLT. This means that the laminar component (giant cells) of stellar turbulent convection indeed plays an important role in the physics of stars. Additionally, we estimate the diffusivity of magnetic elements in stellar photospheres.
NASA Astrophysics Data System (ADS)
Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Méndez, Fernando
2014-12-01
The estimation of possible impacts related to climate change on the wave climate is subject to several levels of uncertainty. In this work, we focus on the uncertainties inherent in the method applied to project the wave climate using atmospheric simulations. Two approaches are commonly used to obtain the regional wave climate: dynamical and statistical downscaling from atmospheric data. We apply both approaches based on the outputs of a global climate model (GCM), ARPEGE-CLIMAT, under three possible future scenarios (B1, A1B and A2) of the Fourth Assessment Report, AR4 (IPCC, 2007), along the French coast and evaluate their results for the wave climate with a high level of precision. The performance of the dynamical and the statistical methods is determined through a comparative analysis of the estimated means, standard deviations and monthly quantile distributions of significant wave heights, the joint probability distributions of wave parameters and seasonal and interannual variability. Analysis of the results shows that the statistical projections are able to reproduce the wave climatology as well as the dynamical projections, with some deficiencies being observed in the summer and for the upper tail of the significant wave height. In addition, with its low computational time requirements, the statistical downscaling method allows an ensemble of simulations to be calculated faster than the dynamical method. It then becomes possible to quantify the uncertainties associated with the choice of the GCM or the socio-economic scenarios, which will improve estimates of the impact of wave climate change along the French coast.
Spatial Statistics Preserving Interpolation Methods for Estimation of Missing Precipitation Data
NASA Astrophysics Data System (ADS)
El Sharif, H.; Teegavarapu, R. S.
2011-12-01
Spatial interpolation methods used for estimation of missing precipitation data at a site seldom check for their ability to preserve site and regional statistics. Such statistics are primarily defined by spatial correlations and other site-to-site statistics in a region. Preservation of site and regional statistics represents a means of assessing the validity of missing precipitation estimates at a site. This study will evaluate the efficacy of traditional deterministic and stochastic interpolation methods aimed at estimation of missing data in preserving site and regional statistics. New optimal spatial interpolation methods that are intended to preserve these statistics are also proposed and evaluated in this study. Rain gauge sites in the state of Kentucky, USA, are used as a case study for evaluation of existing and newly proposed methods. Several error and performance measures will be used to evaluate the methods and trade-offs in accuracy of estimation and preservation of site and regional statistics.
NASA Astrophysics Data System (ADS)
Sadovich, Sergey; Talamo, A.; Burnos, V.; Kiyavitskaya, H.; Fokov, Yu.
2014-06-01
In subcritical systems driven by an external neutron source, the experimental methods based on pulsed neutron source and statistical techniques play an important role for reactivity measurement. Simulation of these methods is very time-consumed procedure. For simulations in Monte-Carlo programs several improvements for neutronic calculations have been made. This paper introduces a new method for simulation PNS and statistical measurements. In this method all events occurred in the detector during simulation are stored in a file using PTRAC feature in the MCNP. After that with a special code (or post-processing) PNS and statistical methods can be simulated. Additionally different shapes of neutron pulses and its lengths as well as dead time of detectors can be included into simulation. The methods described above were tested on subcritical assembly Yalina-Thermal, located in Joint Institute for Power and Nuclear Research SOSNY, Minsk, Belarus. A good agreement between experimental and simulated results was shown.
Applying Statistical Methods To The Proton Radius Puzzle
NASA Astrophysics Data System (ADS)
Higinbotham, Douglas
2016-03-01
In recent nuclear physics publications, one can find many examples where chi2 and reduced chi2 are the only tools used for the selection of models even though a chi2 difference test is only meaningful for nested models. With this in mind, we reanalyze electron scattering data, being careful to clearly define our selection criteria as well as using a co-variance matrix and confidence levels as per the statistics section of the particle data book. We will show that when applying such techniques to hydrogen elastic scattering data, the nested models often require fewer parameters than typically used and that non-nested models are often rejected inappropriately.
Research design and statistical methods in Pakistan Journal of Medical Sciences (PJMS)
Akhtar, Sohail; Shah, Syed Wadood Ali; Rafiq, M.; Khan, Ajmal
2016-01-01
Objective: This article compares the study design and statistical methods used in 2005, 2010 and 2015 of Pakistan Journal of Medical Sciences (PJMS). Methods: Only original articles of PJMS were considered for the analysis. The articles were carefully reviewed for statistical methods and designs, and then recorded accordingly. The frequency of each statistical method and research design was estimated and compared with previous years. Results: A total of 429 articles were evaluated (n=74 in 2005, n=179 in 2010, n=176 in 2015) in which 171 (40%) were cross-sectional and 116 (27%) were prospective study designs. A verity of statistical methods were found in the analysis. The most frequent methods include: descriptive statistics (n=315, 73.4%), chi-square/Fisher’s exact tests (n=205, 47.8%) and student t-test (n=186, 43.4%). There was a significant increase in the use of statistical methods over time period: t-test, chi-square/Fisher’s exact test, logistic regression, epidemiological statistics, and non-parametric tests. Conclusion: This study shows that a diverse variety of statistical methods have been used in the research articles of PJMS and frequency improved from 2005 to 2015. However, descriptive statistics was the most frequent method of statistical analysis in the published articles while cross-sectional study design was common study design. PMID:27022365
A robust vector field correction method via a mixture statistical model of PIV signal
NASA Astrophysics Data System (ADS)
Lee, Yong; Yang, Hua; Yin, Zhouping
2016-03-01
Outlier (spurious vector) is a common problem in practical velocity field measurement using particle image velocimetry technology (PIV), and it should be validated and replaced by a reliable value. One of the most challenging problems is to correctly label the outliers under the circumstance that measurement noise exists or the flow becomes turbulent. Moreover, the outlier's cluster occurrence makes it difficult to pick out all the outliers. Most of current methods validate and correct the outliers using local statistical models in a single pass. In this work, a vector field correction (VFC) method is proposed directly from a mixture statistical model of PIV signal. Actually, this problem is formulated as a maximum a posteriori (MAP) estimation of a Bayesian model with hidden/latent variables, labeling the outliers in the original field. The solution of this MAP estimation, i.e., the outlier set and the restored flow field, is optimized iteratively using an expectation-maximization algorithm. We illustrated this VFC method on two kinds of synthetic velocity fields and two kinds of experimental data and demonstrated that it is robust to a very large number of outliers (even up to 60 %). Besides, the proposed VFC method has high accuracy and excellent compatibility for clustered outliers, compared with the state-of-the-art methods. Our VFC algorithm is computationally efficient, and corresponding Matlab code is provided for others to use it. In addition, our approach is general and can be seamlessly extended to three-dimensional-three-component (3D3C) PIV data.
Statistical methods for the analysis of climate extremes
NASA Astrophysics Data System (ADS)
Naveau, Philippe; Nogaj, Marta; Ammann, Caspar; Yiou, Pascal; Cooley, Daniel; Jomelli, Vincent
2005-08-01
Currently there is an increasing research activity in the area of climate extremes because they represent a key manifestation of non-linear systems and an enormous impact on economic and social human activities. Our understanding of the mean behavior of climate and its 'normal' variability has been improving significantly during the last decades. In comparison, climate extreme events have been hard to study and even harder to predict because they are, by definition, rare and obey different statistical laws than averages. In this context, the motivation for this paper is twofold. Firstly, we recall the basic principles of Extreme Value Theory that is used on a regular basis in finance and hydrology, but it still does not have the same success in climate studies. More precisely, the theoretical distributions of maxima and large peaks are recalled. The parameters of such distributions are estimated with the maximum likelihood estimation procedure that offers the flexibility to take into account explanatory variables in our analysis. Secondly, we detail three case-studies to show that this theory can provide a solid statistical foundation, specially when assessing the uncertainty associated with extreme events in a wide range of applications linked to the study of our climate. To cite this article: P. Naveau et al., C. R. Geoscience 337 (2005).
Students' Attitudes toward Statistics across the Disciplines: A Mixed-Methods Approach
ERIC Educational Resources Information Center
Griffith, James D.; Adams, Lea T.; Gu, Lucy L.; Hart, Christian L.; Nichols-Whitehead, Penney
2012-01-01
Students' attitudes toward statistics were investigated using a mixed-methods approach including a discovery-oriented qualitative methodology among 684 undergraduate students across business, criminal justice, and psychology majors where at least one course in statistics was required. Students were asked about their attitudes toward statistics and…
ERIC Educational Resources Information Center
Chamberlain, John Martyn; Hillier, John; Signoretta, Paola
2015-01-01
This article reports the results of research concerned with students' statistical anxiety and confidence to both complete and learn to complete statistical tasks. Data were collected at the beginning and end of a quantitative method statistics module. Students recognised the value of numeracy skills but felt they were not necessarily relevant for…
Statistical Methods and Tools for Hanford Staged Feed Tank Sampling
Fountain, Matthew S.; Brigantic, Robert T.; Peterson, Reid A.
2013-10-01
This report summarizes work conducted by Pacific Northwest National Laboratory to technically evaluate the current approach to staged feed sampling of high-level waste (HLW) sludge to meet waste acceptance criteria (WAC) for transfer from tank farms to the Hanford Waste Treatment and Immobilization Plant (WTP). The current sampling and analysis approach is detailed in the document titled Initial Data Quality Objectives for WTP Feed Acceptance Criteria, 24590-WTP-RPT-MGT-11-014, Revision 0 (Arakali et al. 2011). The goal of this current work is to evaluate and provide recommendations to support a defensible, technical and statistical basis for the staged feed sampling approach that meets WAC data quality objectives (DQOs).
Statistical methods and neural network approaches for classification of data from multiple sources
NASA Technical Reports Server (NTRS)
Benediktsson, Jon Atli; Swain, Philip H.
1990-01-01
Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.
Multilevel additive Schwarz method for the h-p version of the Galerkin boundary element method
NASA Astrophysics Data System (ADS)
Heuer, N.; Stephan, E. P.; Tran, T.
1998-04-01
We study a multilevel additive Schwarz method for the h-p version of the Galerkin boundary element method with geometrically graded meshes. Both hypersingular and weakly singular integral equations of the first kind are considered. As it is well known the h-p version with geometric meshes converges exponentially fast in the energy-norm. However, the condition number of the Galerkin matrix in this case blows up exponentially in the number of unknowns M. We prove that the condition number kappa(P) of the multilevel additive Schwarz operator behaves like O(root Mlog(2) M). Asa direct consequence of this we also give the results for the 2-level preconditioner and also for the h-p version with quasi-uniform meshes. Numerical results supporting our theory are presented.
Statistical energy analysis response prediction methods for structural systems
NASA Technical Reports Server (NTRS)
Davis, R. F.
1979-01-01
The results of an effort to document methods for accomplishing response predictions for commonly encountered aerospace structural configurations is presented. Application of these methods to specified aerospace structure to provide sample analyses is included. An applications manual, with the structural analyses appended as example problems is given. Comparisons of the response predictions with measured data are provided for three of the example problems.
Data Analysis & Statistical Methods for Command File Errors
NASA Technical Reports Server (NTRS)
Meshkat, Leila; Waggoner, Bruce; Bryant, Larry
2014-01-01
This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.
Exploring the use of statistical process control methods to assess course changes
NASA Astrophysics Data System (ADS)
Vollstedt, Ann-Marie
This dissertation pertains to the field of Engineering Education. The Department of Mechanical Engineering at the University of Nevada, Reno (UNR) is hosting this dissertation under a special agreement. This study was motivated by the desire to find an improved, quantitative measure of student quality that is both convenient to use and easy to evaluate. While traditional statistical analysis tools such as ANOVA (analysis of variance) are useful, they are somewhat time consuming and are subject to error because they are based on grades, which are influenced by numerous variables, independent of student ability and effort (e.g. inflation and curving). Additionally, grades are currently the only measure of quality in most engineering courses even though most faculty agree that grades do not accurately reflect student quality. Based on a literature search, in this study, quality was defined as content knowledge, cognitive level, self efficacy, and critical thinking. Nineteen treatments were applied to a pair of freshmen classes in an effort in increase the qualities. The qualities were measured via quiz grades, essays, surveys, and online critical thinking tests. Results from the quality tests were adjusted and filtered prior to analysis. All test results were subjected to Chauvenet's criterion in order to detect and remove outlying data. In addition to removing outliers from data sets, it was felt that individual course grades needed adjustment to accommodate for the large portion of the grade that was defined by group work. A new method was developed to adjust grades within each group based on the residual of the individual grades within the group and the portion of the course grade defined by group work. It was found that the grade adjustment method agreed 78% of the time with the manual ii grade changes instructors made in 2009, and also increased the correlation between group grades and individual grades. Using these adjusted grades, Statistical Process Control
Nedic, Vladimir; Despotovic, Danijela; Cvetanovic, Slobodan; Despotovic, Milan; Babic, Sasa
2014-11-15
Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
Statistical method for resolving the photon-photoelectron-counting inversion problem
Wu Jinlong; Li Tiejun; Peng, Xiang; Guo Hong
2011-02-01
A statistical inversion method is proposed for the photon-photoelectron-counting statistics in quantum key distribution experiment. With the statistical viewpoint, this problem is equivalent to the parameter estimation for an infinite binomial mixture model. The coarse-graining idea and Bayesian methods are applied to deal with this ill-posed problem, which is a good simple example to show the successful application of the statistical methods to the inverse problem. Numerical results show the applicability of the proposed strategy. The coarse-graining idea for the infinite mixture models should be general to be used in the future.
Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors
Ortiz, Andrés; Palacio, Antonio A.; Górriz, Juan M.; Ramírez, Javier; Salas-González, Diego
2013-01-01
Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD) systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE) and has been assessed using real brain images from the Internet Brain Image Repository (IBSR). PMID:23762192
Agreement between Statistical and Judgmental Item Bias Methods.
ERIC Educational Resources Information Center
Rengel, Elizabeth
The Ball Aptitude Battery (BAB) was examined for item bias in a sample of 577 high school students in which males and females, as well as three ethnic groups (Blacks, Whites, and Hispanics) were represented. The objectives of the investigation were: (1) to assess the level of interrater agreement for the judgmental method; (2) to find the level of…
Scalar Product Method in Statistical Mechanics of Boundary Tension
NASA Astrophysics Data System (ADS)
Cenedese, Pierre; Kikuchi, Ryoichi
1997-02-01
The interphase excess free energy σ due to an interphase boundary (IPB) is calculated in the Ising model using the Scalar Product (SP) method. Different from the “sum” method calculation of σ based on the boundary profile, the SP approach skips the profile and directly evaluates σ from the equilibrium properties of the homogeneous phases meeting at the boundary. Using a series of Cluster Variation Method (CVM) approximations of the basic cluster size n, a series of σ (n) values are calculated. For the 2-D square lattice, the limit of the SP σ (n) for nrightarrow infty is very close to the exact value of Onsager for the <~ngle 10rangle orientation and to that of Fisher and Ferdinand for <~ngle 10rangle. Similar extrapolation was done for the 3-D simple cubic lattice. The result agrees well with the known Monte Carlo results. Because the SP approach does not calculate the profile, computational time and labor are much less than those of the sum method.
Interactive statistical-distribution-analysis program utilizing numerical and graphical methods
Glandon, S. R.; Fields, D. E.
1982-04-01
The TERPED/P program is designed to facilitate the quantitative analysis of experimental data, determine the distribution function that best describes the data, and provide graphical representations of the data. This code differs from its predecessors, TEDPED and TERPED, in that a printer-plotter has been added for graphical output flexibility. The addition of the printer-plotter provides TERPED/P with a method of generating graphs that is not dependent on DISSPLA, Integrated Software Systems Corporation's confidential proprietary graphics package. This makes it possible to use TERPED/P on systems not equipped with DISSPLA. In addition, the printer plot is usually produced more rapidly than a high-resolution plot can be generated. Graphical and numerical tests are performed on the data in accordance with the user's assumption of normality or lognormality. Statistical analysis options include computation of the chi-squared statistic and its significance level and the Kolmogorov-Smirnov one-sample test confidence level for data sets of more than 80 points. Plots can be produced on a Calcomp paper plotter, a FR80 film plotter, or a graphics terminal using the high-resolution, DISSPLA-dependent plotter or on a character-type output device by the printer-plotter. The plots are of cumulative probability (abscissa) versus user-defined units (ordinate). The program was developed on a Digital Equipment Corporation (DEC) PDP-10 and consists of 1500 statements. The language used is FORTRAN-10, DEC's extended version of FORTRAN-IV.
New Developments in the Embedded Statistical Coupling Method: Atomistic/Continuum Crack Propagation
NASA Technical Reports Server (NTRS)
Saether, E.; Yamakov, V.; Glaessgen, E.
2008-01-01
A concurrent multiscale modeling methodology that embeds a molecular dynamics (MD) region within a finite element (FEM) domain has been enhanced. The concurrent MD-FEM coupling methodology uses statistical averaging of the deformation of the atomistic MD domain to provide interface displacement boundary conditions to the surrounding continuum FEM region, which, in turn, generates interface reaction forces that are applied as piecewise constant traction boundary conditions to the MD domain. The enhancement is based on the addition of molecular dynamics-based cohesive zone model (CZM) elements near the MD-FEM interface. The CZM elements are a continuum interpretation of the traction-displacement relationships taken from MD simulations using Cohesive Zone Volume Elements (CZVE). The addition of CZM elements to the concurrent MD-FEM analysis provides a consistent set of atomistically-based cohesive properties within the finite element region near the growing crack. Another set of CZVEs are then used to extract revised CZM relationships from the enhanced embedded statistical coupling method (ESCM) simulation of an edge crack under uniaxial loading.
ERIC Educational Resources Information Center
Ossai, Peter Agbadobi Uloku
2016-01-01
This study examined the relationship between students' scores on Research Methods and statistics, and undergraduate project at the final year. The purpose was to find out whether students matched knowledge of research with project-writing skill. The study adopted an expost facto correlational design. Scores on Research Methods and Statistics for…
APA's Learning Objectives for Research Methods and Statistics in Practice: A Multimethod Analysis
ERIC Educational Resources Information Center
Tomcho, Thomas J.; Rice, Diana; Foels, Rob; Folmsbee, Leah; Vladescu, Jason; Lissman, Rachel; Matulewicz, Ryan; Bopp, Kara
2009-01-01
Research methods and statistics courses constitute a core undergraduate psychology requirement. We analyzed course syllabi and faculty self-reported coverage of both research methods and statistics course learning objectives to assess the concordance with APA's learning objectives (American Psychological Association, 2007). We obtained a sample of…
Best Practices in Teaching Statistics and Research Methods in the Behavioral Sciences [with CD-ROM
ERIC Educational Resources Information Center
Dunn, Dana S., Ed.; Smith, Randolph A., Ed.; Beins, Barney, Ed.
2007-01-01
This book provides a showcase for "best practices" in teaching statistics and research methods in two- and four-year colleges and universities. A helpful resource for teaching introductory, intermediate, and advanced statistics and/or methods, the book features coverage of: (1) ways to integrate these courses; (2) how to promote ethical conduct;…
New Statistical Methods for the Analysis of the Cratering on Venus
NASA Astrophysics Data System (ADS)
Xie, M.; Smrekar, S. E.; Handcock, M. S.
2014-12-01
The sparse crater population (~1000 craters) on Venus is the most important clue of determining the planet's surface age and aids in understanding its geologic history. What processes (volcanism, tectonism, weathering, etc.) modify the total impact crater population? Are the processes regional or global in occurrence? The heated debate on these questions points to the need for better approaches. We present new statistical methods for the analysis of the crater locations and characteristics. Specifically: 1) We produce a map of crater density and the proportion of no halo craters (inferred to be modified) by using generalized additive models, and smoothing splines with a spherical spline basis set. Based on this map, we are able to predict the probability of a crater has no halo given that there is a crater at that point. We also obtain a continuous representation of the ratio of craters with no halo as a function of crater density. This approach allows us to look for regions that appear to have experienced more or less modification, and are thus potentially older or younger. 2) We examine the randomness or clustering of distributions of craters by type (e.g. dark floored, intermediate). For example, for dark floored craters we consider two hypotheses: i) the dark floored craters are randomly distributed on the surface; ii) the dark floored craters are random given the locations of the crater population. Instead of only using a single measure such as average nearest neighbor distance, we use the probability density function of these distances, and compare it to complete spatial randomness to get the relative probability density function. This function gives us a clearer picture of how and where the nearest neighbor distances differ from complete spatial randomness. We also conduct statistical tests of these hypotheses. Confidence intervals with specified global coverage are constructed. Software to reproduce the methods is available in the open source statistics
Salvatore, Stefania; Bramness, Jørgen Gustav; Reid, Malcolm J.; Thomas, Kevin Victor; Harman, Christopher; Røislien, Jo
2015-01-01
Background Wastewater-based epidemiology (WBE) is a new methodology for estimating the drug load in a population. Simple summary statistics and specification tests have typically been used to analyze WBE data, comparing differences between weekday and weekend loads. Such standard statistical methods may, however, overlook important nuanced information in the data. In this study, we apply functional data analysis (FDA) to WBE data and compare the results to those obtained from more traditional summary measures. Methods We analysed temporal WBE data from 42 European cities, using sewage samples collected daily for one week in March 2013. For each city, the main temporal features of two selected drugs were extracted using functional principal component (FPC) analysis, along with simpler measures such as the area under the curve (AUC). The individual cities’ scores on each of the temporal FPCs were then used as outcome variables in multiple linear regression analysis with various city and country characteristics as predictors. The results were compared to those of functional analysis of variance (FANOVA). Results The three first FPCs explained more than 99% of the temporal variation. The first component (FPC1) represented the level of the drug load, while the second and third temporal components represented the level and the timing of a weekend peak. AUC was highly correlated with FPC1, but other temporal characteristic were not captured by the simple summary measures. FANOVA was less flexible than the FPCA-based regression, and even showed concordance results. Geographical location was the main predictor for the general level of the drug load. Conclusion FDA of WBE data extracts more detailed information about drug load patterns during the week which are not identified by more traditional statistical methods. Results also suggest that regression based on FPC results is a valuable addition to FANOVA for estimating associations between temporal patterns and covariate
A REVIEW OF STATISTICAL METHODS FOR THE METEOROLOGICAL ADJUSTMENT OF TROPOSPHERIC OZONE
A variety of statistical methods for meteorological adjustment of ozone have been proposed in the literature over the last decade for purposes of forecasting, estimating ozone time trends, or investigating underlying mechanisms from an empirical perspective. The methods can be...
Statistical classification methods for estimating ancestry using morphoscopic traits.
Hefner, Joseph T; Ousley, Stephen D
2014-07-01
Ancestry assessments using cranial morphoscopic traits currently rely on subjective trait lists and observer experience rather than empirical support. The trait list approach, which is untested, unverified, and in many respects unrefined, is relied upon because of tradition and subjective experience. Our objective was to examine the utility of frequently cited morphoscopic traits and to explore eleven appropriate and novel methods for classifying an unknown cranium into one of several reference groups. Based on these results, artificial neural networks (aNNs), OSSA, support vector machines, and random forest models showed mean classification accuracies of at least 85%. The aNNs had the highest overall classification rate (87.8%), and random forests show the smallest difference between the highest (90.4%) and lowest (76.5%) classification accuracies. The results of this research demonstrate that morphoscopic traits can be successfully used to assess ancestry without relying only on the experience of the observer. PMID:24646108
Predicting sulphur and nitrogen deposition using a simple statistical method
NASA Astrophysics Data System (ADS)
Filip, Oulehle; Jiří, Kopáček; Tomáš, Chuman; Vladimír, Černohous; Iva, Hůnová; Jakub, Hruška; Pavel, Krám; Zora, Lachmanová; Tomáš, Navrátil; Petr, Štěpánek; Miroslav, Tesař; Christopher, Evans D.
2016-09-01
Data from 32 long-term (1994-2012) monitoring sites were used to assess temporal development and spatial variability of sulphur (S) and inorganic nitrogen (N) concentrations in bulk precipitation, and S in throughfall, for the Czech Republic. Despite large variance in absolute S and N concentration/deposition among sites, temporal coherence using standardised data (Z score) was demonstrated. Overall significant declines of SO4 concentration in bulk and throughfall precipitation, as well as NO3 and NH4 concentration in bulk precipitation, were observed. Median Z score values of bulk SO4, NO3 and NH4 and throughfall SO4 derived from observations and the respective emission rates of SO2, NOx and NH3 in the Czech Republic and Slovakia showed highly significant (p < 0.001) relationships. Using linear regression models, Z score values were calculated for the whole period 1900-2012 and then back-transformed to give estimates of concentration for the individual sites. Uncertainty associated with the concentration calculations was estimated as 20% for SO4 bulk precipitation, 22% for throughfall SO4, 18% for bulk NO3 and 28% for bulk NH4. The application of the method suggested that it is effective in the long-term reconstruction and prediction of S and N deposition at a variety of sites. Multiple regression modelling was used to extrapolate site characteristics (mean precipitation chemistry and its standard deviation) from monitored to unmonitored sites. Spatially distributed temporal development of S and N depositions were calculated since 1900. The method allows spatio-temporal estimation of the acid deposition in regions with extensive monitoring of precipitation chemistry.
A Tool Preference Choice Method for RNA Secondary Structure Prediction by SVM with Statistical Tests
Hor, Chiou-Yi; Yang, Chang-Biau; Chang, Chia-Hung; Tseng, Chiou-Ting; Chen, Hung-Hsin
2013-01-01
The Prediction of RNA secondary structures has drawn much attention from both biologists and computer scientists. Many useful tools have been developed for this purpose. These tools have their individual strengths and weaknesses. As a result, based on support vector machines (SVM), we propose a tool choice method which integrates three prediction tools: pknotsRG, RNAStructure, and NUPACK. Our method first extracts features from the target RNA sequence, and adopts two information-theoretic feature selection methods for feature ranking. We propose a method to combine feature selection and classifier fusion in an incremental manner. Our test data set contains 720 RNA sequences, where 225 pseudoknotted RNA sequences are obtained from PseudoBase, and 495 nested RNA sequences are obtained from RNA SSTRAND. The method serves as a preprocessing way in analyzing RNA sequences before the RNA secondary structure prediction tools are employed. In addition, the performance of various configurations is subject to statistical tests to examine their significance. The best base-pair accuracy achieved is 75.5%, which is obtained by the proposed incremental method, and is significantly higher than 68.8%, which is associated with the best predictor, pknotsRG. PMID:23641141
Stanley, Jeffrey R.; Adkins, Joshua N.; Slysz, Gordon W.; Monroe, Matthew E.; Purvine, Samuel O.; Karpievitch, Yuliya V.; Anderson, Gordon A.; Smith, Richard D.; Dabney, Alan R.
2011-07-15
High-throughput proteomics is rapidly evolving to require high mass measurement accuracy for a variety of different applications. Increased mass measurement accuracy in bottom-up proteomics specifically allows for an improved ability to distinguish and characterize detected MS features, which may in turn be identified by, e.g., matching to entries in a database for both precursor and fragmentation mass identification methods. Many tools exist with which to score the identification of peptides from LC-MS/MS measurements or to assess matches to an accurate mass and time (AMT) tag database, but these two calculations remain distinctly unrelated. Here we present a statistical method, Statistical Tools for AMT tag Confidence (STAC), which extends our previous work incorporating prior probabilities of correct sequence identification from LC-MS/MS, as well as the quality with which LC-MS features match AMT tags, to evaluate peptide identification confidence. Compared to existing tools, we are able to obtain significantly more high-confidence peptide identifications at a given false discovery rate and additionally assign confidence estimates to individual peptide identifications. Freely available software implementations of STAC are available in both command line and as a Windows graphical application.
Statistical methods for the forensic analysis of striated tool marks
Hoeksema, Amy Beth
2013-01-01
In forensics, fingerprints can be used to uniquely identify suspects in a crime. Similarly, a tool mark left at a crime scene can be used to identify the tool that was used. However, the current practice of identifying matching tool marks involves visual inspection of marks by forensic experts which can be a very subjective process. As a result, declared matches are often successfully challenged in court, so law enforcement agencies are particularly interested in encouraging research in more objective approaches. Our analysis is based on comparisons of profilometry data, essentially depth contours of a tool mark surface taken along a linear path. In current practice, for stronger support of a match or non-match, multiple marks are made in the lab under the same conditions by the suspect tool. We propose the use of a likelihood ratio test to analyze the difference between a sample of comparisons of lab tool marks to a field tool mark, against a sample of comparisons of two lab tool marks. Chumbley et al. (2010) point out that the angle of incidence between the tool and the marked surface can have a substantial impact on the tool mark and on the effectiveness of both manual and algorithmic matching procedures. To better address this problem, we describe how the analysis can be enhanced to model the effect of tool angle and allow for angle estimation for a tool mark left at a crime scene. With sufficient development, such methods may lead to more defensible forensic analyses.
ERIC Educational Resources Information Center
Fernandez, Ceneida; Llinares, Salvador; Van Dooren, Wim; De Bock, Dirk; Verschaffel, Lieven
2012-01-01
This study investigates the development of proportional and additive methods along primary and secondary school. In particular, it simultaneously investigates the use of additive methods in proportional word problems and the use of proportional methods in additive word problems. We have also studied the role played by integer and non-integer…
INTERMAP: background, aims, design, methods, and descriptive statistics (nondietary).
Stamler, J; Elliott, P; Dennis, B; Dyer, A R; Kesteloot, H; Liu, K; Ueshima, H; Zhou, B F
2003-09-01
Blood pressure (BP) above optimal (< or =120/< or =80 mmHg) is established as a major cardiovascular disease (CVD) risk factor. Prevalence of adverse BP is high in most adult populations; until recently research has been sparse on reasons for this. Since the 1980s, epidemiologic studies confirmed that salt, alcohol intake, and body mass relate directly to BP; dietary potassium, inversely. Several other nutrients also probably influence BP. The DASH feeding trials demonstrated that with the multiple modifications in the DASH combination diet, SBP/DBP (SBP: systolic blood pressure, DBP: diastolic blood pressure) was sizably reduced, independent of calorie balance, alcohol intake, and BP reduction with decreased dietary salt. A key challenge for research is to elucidate specific nutrients accounting for this effect. The general aim of the study was to clarify influences of multiple nutrients on SBP/DBP of individuals over and above effects of Na, K, alcohol, and body mass. Specific aims were, in a cross-sectional epidemiologic study of 4680 men and women aged 40-59 years from 17 diverse population samples in China, Japan, UK, and USA, test 10 prior hypotheses on relations of macronutrients to SBP/DBP and on role of dietary factors in inverse associations of education with BP; test four related subgroup hypotheses; explore associations with SBP/DBP of multiple other nutrients, urinary metabolites, and foods. For these purposes, for all 4680 participants, with standardized high-quality methods, assess individual intake of 76 nutrients from four 24-h dietary recalls/person; measure in two timed 24-h urine collections/person 24-h excretion of Na, K, Ca, Mg, creatinine, amino acids; microalbuminuria; multiple nutrients and metabolites by nuclear magnetic resonance and high-pressure liquid chromatography. Based on eight SBP/DBP measurements/person, and data on multiple possible confounders, utilize mainly multiple linear regression and quantile analyses to test prior
A statistical gap-filling method to interpolate global monthly surface ocean carbon dioxide data
NASA Astrophysics Data System (ADS)
Jones, Steve D.; Le Quéré, Corinne; Rödenbeck, Christian; Manning, Andrew C.; Olsen, Are
2015-12-01
We have developed a statistical gap-filling method adapted to the specific coverage and properties of observed fugacity of surface ocean CO2 (fCO2). We have used this method to interpolate the Surface Ocean CO2 Atlas (SOCAT) v2 database on a 2.5°×2.5° global grid (south of 70°N) for 1985-2011 at monthly resolution. The method combines a spatial interpolation based on a "radius of influence" to determine nearby similar fCO2 values with temporal harmonic and cubic spline curve-fitting, and also fits long-term trends and seasonal cycles. Interannual variability is established using deviations of observations from the fitted trends and seasonal cycles. An uncertainty is computed for all interpolated values based on the spatial and temporal range of the interpolation. Tests of the method using model data show that it performs as well as or better than previous regional interpolation methods, but in addition it provides a near-global and interannual coverage.
NASA Technical Reports Server (NTRS)
Xu, Kuan-Man
2006-01-01
A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called cloud objects. Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.
ERIC Educational Resources Information Center
Kibishi, Hiroshi; Hirabayashi, Kuniaki; Nakagawa, Seiichi
2015-01-01
In this paper, we propose a statistical evaluation method of pronunciation proficiency and intelligibility for presentations made in English by native Japanese speakers. We statistically analyzed the actual utterances of speakers to find combinations of acoustic and linguistic features with high correlation between the scores estimated by the…
ERIC Educational Resources Information Center
Panizzon, D. L.; Boulton, A. J.
2004-01-01
To undertake rigorous research in biology and ecology, students must be able to pose testable hypotheses, design decisive studies, and analyse results using suitable statistics. Yet, few biology students excel in topics involving statistics and most attempt to evade optional courses in research methods. Over the last few years, we have developed…
Statistical method for determining and comparing limits of detection of bioassays.
Holstein, Carly A; Griffin, Maryclare; Hong, Jing; Sampson, Paul D
2015-10-01
The current bioassay development literature lacks the use of statistically robust methods for calculating the limit of detection of a given assay. Instead, researchers often employ simple methods that provide a rough estimate of the limit of detection, often without a measure of the confidence in the estimate. This scarcity of robust methods is likely due to a realistic preference for simple and accessible methods and to a lack of such methods that have reduced the concepts of limit of detection theory to practice for the specific application of bioassays. Here, we have developed a method for determining limits of detection for bioassays that is statistically robust and reduced to practice in a clear and accessible manner geared at researchers, not statisticians. This method utilizes a four-parameter logistic curve fit to translate signal intensity to analyte concentration, which is a curve that is commonly employed in quantitative bioassays. This method generates a 95% confidence interval of the limit of detection estimate to provide a measure of uncertainty and a means by which to compare the analytical sensitivities of different assays statistically. We have demonstrated this method using real data from the development of a paper-based influenza assay in our laboratory to illustrate the steps and features of the method. Using this method, assay developers can calculate statistically valid limits of detection and compare these values for different assays to determine when a change to the assay design results in a statistically significant improvement in analytical sensitivity. PMID:26376354
Physics-based statistical model and simulation method of RF propagation in urban environments
Pao, Hsueh-Yuan; Dvorak, Steven L.
2010-09-14
A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.
Technology Transfer Automated Retrieval System (TEKTRAN)
Normalization methods used in the statistical analysis of oligonucleotide microarray data were evaluated. The oligonucleotide microarray is considered an efficient analytical tool for analyzing thousands of genes simultaneously in a single experiment. However, systematic variation in microarray, ori...
Gadbury, G L; Coffey, C S; Allison, D B
2003-08-01
This paper brings together some modern statistical methods to address the problem of missing data in obesity trials with repeated measurements. Such missing data occur when subjects miss one or more follow-up visits, or drop out early from an obesity trial. A common approach to dealing with missing data because of dropout is 'last observation carried forward' (LOCF). This method, although intuitively appealing, requires restrictive assumptions to produce valid statistical conclusions. We review the need for obesity trials, the assumptions that must be made regarding missing data in such trials, and some modern statistical methods for analysing data containing missing repeated measurements. These modern methods have fewer limitations and less restrictive assumptions than required for LOCF. Moreover, their recent introduction into current releases of statistical software and textbooks makes them more readily available to the applied data analyses. PMID:12916818
Recommended methods for statistical analysis of data containing less-than-detectable measurements
Atwood, C.L.; Blackwood, L.G.; Harris, G.A.; Loehr, C.A.
1990-09-01
This report is a manual for statistical workers dealing with environmental measurements, when some of the measurements are not given exactly but are only reported as less than detectable. For some statistical settings with such data, many methods have been proposed in the literature, while for others few or none have been proposed. This report gives a recommended method in each of the settings considered. The body of the report gives a brief description of each recommended method. Appendix A gives example programs using the statistical package SAS, for those methods that involve nonstandard methods. Appendix B presents the methods that were compared and the reasons for selecting each recommended method, and explains any fine points that might be of interest. This is an interim version. Future revisions will complete the recommendations. 34 refs., 2 figs., 11 tabs.
Recommended methods for statistical analysis of data containing less-than-detectable measurements
Atwood, C.L.; Blackwood, L.G.; Harris, G.A.; Loehr, C.A.
1991-09-01
This report is a manual for statistical workers dealing with environmental measurements, when some of the measurements are not given exactly but are only reported as less than detectable. For some statistical settings with such data, many methods have been proposed in the literature, while for others few or none have been proposed. This report gives a recommended method in each of the settings considered. The body of the report gives a brief description of each recommended method. Appendix A gives example programs using the statistical package SAS, for those methods that involve nonstandard methods. Appendix B presents the methods that were compared and the reasons for selecting each recommended method, and explains any fine points that might be of interest. 7 refs., 4 figs.
An improved method for statistical analysis of raw accelerator mass spectrometry data
Gutjahr, A.; Phillips, F.; Kubik, P.W.; Elmore, D.
1987-01-01
Hierarchical statistical analysis is an appropriate method for statistical treatment of raw accelerator mass spectrometry (AMS) data. Using Monte Carlo simulations we show that this method yields more accurate estimates of isotope ratios and analytical uncertainty than the generally used propagation of errors approach. The hierarchical analysis is also useful in design of experiments because it can be used to identify sources of variability. 8 refs., 2 figs.
LaBudde, Robert A; Harnly, James M
2012-01-01
A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given. PMID:22468371
NASA Astrophysics Data System (ADS)
Zhang, Xianliang; Yan, Xiaodong
2015-11-01
A new statistical downscaling method was developed and applied to downscale monthly total precipitation from 583 stations in China. Generally, there are two steps involved in statistical downscaling: first, the predictors are selected (large-scale variables) and transformed; and second, a model between the predictors and the predictand (in this case, precipitation) is established. In the first step, a selection process of the predictor domain, called the optimum correlation method (OCM), was developed to transform the predictors. The transformed series obtained by the OCM showed much better correlation with the predictand than those obtained by the traditional transform method for the same predictor. Moreover, the method combining OCM and linear regression obtained better downscaling results than the traditional linear regression method, suggesting that the OCM could be used to improve the results of statistical downscaling. In the second step, Bayesian model averaging (BMA) was adopted as an alternative to linear regression. The method combining the OCM and BMA showed much better performance than the method combining the OCM and linear regression. Thus, BMA could be used as an alternative to linear regression in the second step of statistical downscaling. In conclusion, the downscaling method combining OCM and BMA produces more accurate results than the multiple linear regression method when used to statistically downscale large-scale variables.
Statistical Evaluation and Improvement of Methods for Combining Random and Harmonic Loads
NASA Technical Reports Server (NTRS)
Brown, A. M.; McGhee, D. S.
2003-01-01
Structures in many environments experience both random and harmonic excitation. A variety of closed-form techniques has been used in the aerospace industry to combine the loads resulting from the two sources. The resulting combined loads are then used to design for both yield/ultimate strength and high- cycle fatigue capability. This Technical Publication examines the cumulative distribution percentiles obtained using each method by integrating the joint probability density function of the sine and random components. A new Microsoft Excel spreadsheet macro that links with the software program Mathematica to calculate the combined value corresponding to any desired percentile is then presented along with a curve tit to this value. Another Excel macro that calculates the combination using Monte Carlo simulation is shown. Unlike the traditional techniques. these methods quantify the calculated load value with a consistent percentile. Using either of the presented methods can be extremely valuable in probabilistic design, which requires a statistical characterization of the loading. Additionally, since the CDF at high probability levels is very flat, the design value is extremely sensitive to the predetermined percentile; therefore, applying the new techniques can substantially lower the design loading without losing any of the identified structural reliability.
Statistical Evaluation and Improvement of Methods for Combining Random and Harmonic Loads
NASA Astrophysics Data System (ADS)
Brown, A. M.; McGhee, D. S.
2003-02-01
Structures in many environments experience both random and harmonic excitation. A variety of closed-form techniques has been used in the aerospace industry to combine the loads resulting from the two sources. The resulting combined loads are then used to design for both yield/ultimate strength and high- cycle fatigue capability. This Technical Publication examines the cumulative distribution percentiles obtained using each method by integrating the joint probability density function of the sine and random components. A new Microsoft Excel spreadsheet macro that links with the software program Mathematica to calculate the combined value corresponding to any desired percentile is then presented along with a curve tit to this value. Another Excel macro that calculates the combination using Monte Carlo simulation is shown. Unlike the traditional techniques. these methods quantify the calculated load value with a consistent percentile. Using either of the presented methods can be extremely valuable in probabilistic design, which requires a statistical characterization of the loading. Additionally, since the CDF at high probability levels is very flat, the design value is extremely sensitive to the predetermined percentile; therefore, applying the new techniques can substantially lower the design loading without losing any of the identified structural reliability.
Development and Evaluation of a Hybrid Dynamical-Statistical Downscaling Method
NASA Astrophysics Data System (ADS)
Walton, Daniel Burton
Regional climate change studies usually rely on downscaling of global climate model (GCM) output in order to resolve important fine-scale features and processes that govern local climate. Previous efforts have used one of two techniques: (1) dynamical downscaling, in which a regional climate model is forced at the boundaries by GCM output, or (2) statistical downscaling, which employs historical empirical relationships to go from coarse to fine resolution. Studies using these methods have been criticized because they either dynamical downscaled only a few GCMs, or used statistical downscaling on an ensemble of GCMs, but missed important dynamical effects in the climate change signal. This study describes the development and evaluation of a hybrid dynamical-statstical downscaling method that utilizes aspects of both dynamical and statistical downscaling to address these concerns. The first step of the hybrid method is to use dynamical downscaling to understand the most important physical processes that contribute to the climate change signal in the region of interest. Then a statistical model is built based on the patterns and relationships identified from dynamical downscaling. This statistical model can be used to downscale an entire ensemble of GCMs quickly and efficiently. The hybrid method is first applied to a domain covering Los Angeles Region to generate projections of temperature change between the 2041-2060 and 1981-2000 periods for 32 CMIP5 GCMs. The hybrid method is also applied to a larger region covering all of California and the adjacent ocean. The hybrid method works well in both areas, primarily because a single feature, the land-sea contrast in the warming, controls the overwhelming majority of the spatial detail. Finally, the dynamically downscaled temperature change patterns are compared to those produced by two commonly-used statistical methods, BCSD and BCCA. Results show that dynamical downscaling recovers important spatial features that the
NASA Astrophysics Data System (ADS)
Lassonde, Sylvain; Vrac, Mathieu; Ruelland, Denis; Dezetter, Alain
2014-05-01
The aim of the GICC project "REMedHE" (http://www.remedhe.org) is to evaluate and compare the evolution of water supply capacity under climatic and anthropogenic changes by 2050 on two Mediterranean catchments: the Hérault (South of France) and the Ebro (North East of Spain) catchments. Indeed, the Mediterranean region has been identified as a "hot spot" of climate change, especially for precipitation which is expected to globally decrease while water needs should continue to increase. To perform such a study, it is then necessary to simulate future water flows with hydrological models fed by high-resolution precipitation data representative of the future climate. To generate high-resolution climate simulations, three different statistical downscaling approaches have been applied. The first one consists in a deterministic transfer function based on a Generalized Additive Model (GAM). The second method involves a Stochastic Weather Generator (SWG), simulating local values from probability density functions conditioned by large-scale predictors. The third approach belongs to the "Model Output Statistics" (MOS) family, in bias correcting the large-scale distributions with respect to the local-scale ones, through the Cumulative Distribution Function transform CDFt approach. These statistical downscaling models were calibrated and cross-validated using the SAFRAN dataset (for Hérault catchment), a dataset compiled by HydroSciences Montpellier (for Ebro catchment) as local-scale reference and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis outputs as predictors, over two time periods 1959-1984 and 1985-2010. Cross-validation analysis shows that the inter-annual variability of the yearly sum of precipitation from GAM is close to that from SAFRAN. However, daily variability and occurrence frequency are badly represented by GAM. On the opposite, SWG and one version of CDFt allow both the inter-annual and
Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M
2016-01-01
Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147
Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.
2016-01-01
Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147
NASA Astrophysics Data System (ADS)
Sunyer, M. A.; Hundecha, Y.; Lawrence, D.; Madsen, H.; Willems, P.; Martinkova, M.; Vormoor, K.; Bürger, G.; Hanel, M.; Kriaučiūnienė, J.; Loukas, A.; Osuch, M.; Yücel, I.
2014-06-01
Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods often used in climate change impact studies. Four methods are based on change factors, three are bias correction methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from fifteen regional climate models (RCMs) from the ENSEMBLES project for eleven catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the statistical downscaling methods vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between change factor and bias correction methods. The performance of the bias correction methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and statistical downscaling methods indicates that up to half of the total variance is derived from the statistical downscaling methods. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need of considering an ensemble of both statistical downscaling methods and climate models.
Methods of learning in statistical education: Design and analysis of a randomized trial
NASA Astrophysics Data System (ADS)
Boyd, Felicity Turner
Background. Recent psychological and technological advances suggest that active learning may enhance understanding and retention of statistical principles. A randomized trial was designed to evaluate the addition of innovative instructional methods within didactic biostatistics courses for public health professionals. Aims. The primary objectives were to evaluate and compare the addition of two active learning methods (cooperative and internet) on students' performance; assess their impact on performance after adjusting for differences in students' learning style; and examine the influence of learning style on trial participation. Methods. Consenting students enrolled in a graduate introductory biostatistics course were randomized to cooperative learning, internet learning, or control after completing a pretest survey. The cooperative learning group participated in eight small group active learning sessions on key statistical concepts, while the internet learning group accessed interactive mini-applications on the same concepts. Controls received no intervention. Students completed evaluations after each session and a post-test survey. Study outcome was performance quantified by examination scores. Intervention effects were analyzed by generalized linear models using intent-to-treat analysis and marginal structural models accounting for reported participation. Results. Of 376 enrolled students, 265 (70%) consented to randomization; 69, 100, and 96 students were randomized to the cooperative, internet, and control groups, respectively. Intent-to-treat analysis showed no differences between study groups; however, 51% of students in the intervention groups had dropped out after the second session. After accounting for reported participation, expected examination scores were 2.6 points higher (of 100 points) after completing one cooperative learning session (95% CI: 0.3, 4.9) and 2.4 points higher after one internet learning session (95% CI: 0.0, 4.7), versus
Romero, Vicente Jose; Peterson, Janet S.; Burkhardt, John V.; Gunzburger, Max Donald
2003-09-01
A recently developed Centroidal Voronoi Tessellation (CVT) unstructured sampling method is investigated here to assess its suitability for use in statistical sampling and function integration. CVT efficiently generates a highly uniform distribution of sample points over arbitrarily shaped M-Dimensional parameter spaces. It has recently been shown on several 2-D test problems to provide superior point distributions for generating locally conforming response surfaces. In this paper, its performance as a statistical sampling and function integration method is compared to that of Latin-Hypercube Sampling (LHS) and Simple Random Sampling (SRS) Monte Carlo methods, and Halton and Hammersley quasi-Monte-Carlo sequence methods. Specifically, sampling efficiencies are compared for function integration and for resolving various statistics of response in a 2-D test problem. It is found that on balance CVT performs best of all these sampling methods on our test problems.
NASA Astrophysics Data System (ADS)
Mann, Michael E.; Steinman, Byron A.; Miller, Sonya K.; Frankcombe, Leela M.; England, Matthew H.; Cheung, Anson H.
2016-04-01
The temporary slowdown in large-scale surface warming during the early 2000s has been attributed to both external and internal sources of climate variability. Using semiempirical estimates of the internal low-frequency variability component in Northern Hemisphere, Atlantic, and Pacific surface temperatures in concert with statistical hindcast experiments, we investigate whether the slowdown and its recent recovery were predictable. We conclude that the internal variability of the North Pacific, which played a critical role in the slowdown, does not appear to have been predictable using statistical forecast methods. An additional minor contribution from the North Atlantic, by contrast, appears to exhibit some predictability. While our analyses focus on combining semiempirical estimates of internal climatic variability with statistical hindcast experiments, possible implications for initialized model predictions are also discussed.
Cacuci, Dan G.; Ionescu-Bujor, Mihaela
2004-07-15
Part II of this review paper highlights the salient features of the most popular statistical methods currently used for local and global sensitivity and uncertainty analysis of both large-scale computational models and indirect experimental measurements. These statistical procedures represent sampling-based methods (random sampling, stratified importance sampling, and Latin Hypercube sampling), first- and second-order reliability algorithms (FORM and SORM, respectively), variance-based methods (correlation ratio-based methods, the Fourier Amplitude Sensitivity Test, and the Sobol Method), and screening design methods (classical one-at-a-time experiments, global one-at-a-time design methods, systematic fractional replicate designs, and sequential bifurcation designs). It is emphasized that all statistical uncertainty and sensitivity analysis procedures first commence with the 'uncertainty analysis' stage and only subsequently proceed to the 'sensitivity analysis' stage; this path is the exact reverse of the conceptual path underlying the methods of deterministic sensitivity and uncertainty analysis where the sensitivities are determined prior to using them for uncertainty analysis. By comparison to deterministic methods, statistical methods for uncertainty and sensitivity analysis are relatively easier to develop and use but cannot yield exact values of the local sensitivities. Furthermore, current statistical methods have two major inherent drawbacks as follows: 1. Since many thousands of simulations are needed to obtain reliable results, statistical methods are at best expensive (for small systems) or, at worst, impracticable (e.g., for large time-dependent systems).2. Since the response sensitivities and parameter uncertainties are inherently and inseparably amalgamated in the results produced by these methods, improvements in parameter uncertainties cannot be directly propagated to improve response uncertainties; rather, the entire set of simulations and
Evaluation of xenobiotic impact on urban receiving waters by means of statistical methods.
Musolff, A; Leschik, S; Schafmeister, M-T; Reinstorf, F; Strauch, G; Krieg, R; Schirmer, M
2010-01-01
Xenobiotics in urban receiving waters are an emerging problem. A sound knowledge of xenobiotic input, distribution and fate in the aquatic environment is a prerequisite for risk assessments. Methods to assess the impact of xenobiotics on urban receiving waters should address the diverse characteristics of the target compounds and the spatiotemporal variability of concentrations. Here, we present results from a one-year-monitoring program concerning concentrations of pharmaceuticals, additives from personal care products and industrial chemicals in an urban drainage catchment in untreated and treated wastewater, surface water and groundwater. Univariate and multivariate statistical methods were applied to characterize the xenobiotic concentrations. Correlation and principal component analysis revealed a pronounced pattern of xenobiotics in the surface water samples. The concentrations of several xenobiotics were characterized by a negative proportionality to the water temperature. Therefore, seasonal attenuation is assumed to be a major process influencing the measured concentrations. Moreover, dilution of xenobiotics the surface water was found to significantly influence the concentrations. These two processes control more the xenobiotic occurrence in the surface water than the less pronounced concentration pattern in the wastewater sources. For the groundwater samples, we assume that foremost attenuation processes lead to the found differentiation of xenobiotics. PMID:20706016
NASA Astrophysics Data System (ADS)
Zhang, Jun; Guo, Fan
2015-11-01
Tooth modification technique is widely used in gear industry to improve the meshing performance of gearings. However, few of the present studies on tooth modification considers the influence of inevitable random errors on gear modification effects. In order to investigate the uncertainties of tooth modification amount variations on system's dynamic behaviors of a helical planetary gears, an analytical dynamic model including tooth modification parameters is proposed to carry out a deterministic analysis on the dynamics of a helical planetary gear. The dynamic meshing forces as well as the dynamic transmission errors of the sun-planet 1 gear pair with and without tooth modifications are computed and compared to show the effectiveness of tooth modifications on gear dynamics enhancement. By using response surface method, a fitted regression model for the dynamic transmission error(DTE) fluctuations is established to quantify the relationship between modification amounts and DTE fluctuations. By shifting the inevitable random errors arousing from manufacturing and installing process to tooth modification amount variations, a statistical tooth modification model is developed and a methodology combining Monte Carlo simulation and response surface method is presented for uncertainty analysis of tooth modifications. The uncertainly analysis reveals that the system's dynamic behaviors do not obey the normal distribution rule even though the design variables are normally distributed. In addition, a deterministic modification amount will not definitely achieve an optimal result for both static and dynamic transmission error fluctuation reduction simultaneously.
RAId_DbS: Method for Peptide ID using Database Search with Accurate Statistics
NASA Astrophysics Data System (ADS)
Alves, Gelio; Ogurtsov, Aleksey; Yu, Yi-Kuo
2007-03-01
The key to proteomics studies, essential in systems biology, is peptide identification. Under tandem mass spectrometry, each spectrum generated consists of a list of mass/charge peaks along with their intensities. Software analysis is then required to identify from the spectrum peptide candidates that best interpret the spectrum. The library search, which compares the spectral peaks against theoretical peaks generated by each peptide in a library, is among the most popular methods. This method, although robust, lacks good quantitative statistical underpinning. As we show, many library search algorithms suffer from statistical instability. The need for a better statistical basis prompted us to develop RAId_DbS. Taking into account the skewness in the peak intensity distribution while scoring peptides, RAId_DbS provides an accurate statistical significance assignment to each peptide candidate. RAId_DbS will be a valuable tool especially when one intends to identify proteins through peptide identifications.
Huang, Huei-Chung; Niu, Yi; Qin, Li-Xuan
2015-01-01
Deep sequencing has recently emerged as a powerful alternative to microarrays for the high-throughput profiling of gene expression. In order to account for the discrete nature of RNA sequencing data, new statistical methods and computational tools have been developed for the analysis of differential expression to identify genes that are relevant to a disease such as cancer. In this paper, it is thus timely to provide an overview of these analysis methods and tools. For readers with statistical background, we also review the parameter estimation algorithms and hypothesis testing strategies used in these methods. PMID:26688660
NASA Astrophysics Data System (ADS)
Chukbar, B. K.
2015-12-01
Two methods of modeling a double-heterogeneity fuel are studied: the deterministic positioning and the statistical method CORN of the MCU software package. The effect of distribution of microfuel in a pebble bed on the calculation results is studied. The results of verification of the statistical method CORN for the cases of the microfuel concentration up to 170 cm-3 in a pebble bed are presented. The admissibility of homogenization of the microfuel coating with the graphite matrix is studied. The dependence of the reactivity on the relative location of fuel and graphite spheres in a pebble bed is found.
Chukbar, B. K.
2015-12-15
Two methods of modeling a double-heterogeneity fuel are studied: the deterministic positioning and the statistical method CORN of the MCU software package. The effect of distribution of microfuel in a pebble bed on the calculation results is studied. The results of verification of the statistical method CORN for the cases of the microfuel concentration up to 170 cm{sup –3} in a pebble bed are presented. The admissibility of homogenization of the microfuel coating with the graphite matrix is studied. The dependence of the reactivity on the relative location of fuel and graphite spheres in a pebble bed is found.
A NEW METHOD TO CORRECT FOR FIBER COLLISIONS IN GALAXY TWO-POINT STATISTICS
Guo Hong; Zehavi, Idit; Zheng Zheng
2012-09-10
In fiber-fed galaxy redshift surveys, the finite size of the fiber plugs prevents two fibers from being placed too close to one another, limiting the ability to study galaxy clustering on all scales. We present a new method for correcting such fiber collision effects in galaxy clustering statistics based on spectroscopic observations. The target galaxy sample is divided into two distinct populations according to the targeting algorithm of fiber placement, one free of fiber collisions and the other consisting of collided galaxies. The clustering statistics are a combination of the contributions from these two populations. Our method makes use of observations in tile overlap regions to measure the contributions from the collided population, and to therefore recover the full clustering statistics. The method is rooted in solid theoretical ground and is tested extensively on mock galaxy catalogs. We demonstrate that our method can well recover the projected and the full three-dimensional (3D) redshift-space two-point correlation functions (2PCFs) on scales both below and above the fiber collision scale, superior to the commonly used nearest neighbor and angular correction methods. We discuss potential systematic effects in our method. The statistical correction accuracy of our method is only limited by sample variance, which scales down with (the square root of) the volume probed. For a sample similar to the final SDSS-III BOSS galaxy sample, the statistical correction error is expected to be at the level of 1% on scales {approx}0.1-30 h {sup -1} Mpc for the 2PCFs. The systematic error only occurs on small scales, caused by imperfect correction of collision multiplets, and its magnitude is expected to be smaller than 5%. Our correction method, which can be generalized to other clustering statistics as well, enables more accurate measurements of full 3D galaxy clustering on all scales with galaxy redshift surveys.
ERIC Educational Resources Information Center
Leppink, Jimmie; Broers, Nick J.; Imbos, Tjaart; van der Vleuten, Cees P. M.; Berger, Martijn P. F.
2013-01-01
The current experiment examined the potential effects of the method of propositional manipulation (MPM) as a lecturing method on motivation to learn and conceptual understanding of statistics. MPM aims to help students develop conceptual understanding by guiding them into self-explanation at two different stages: First, at the stage of…
NASA Astrophysics Data System (ADS)
Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.
2009-09-01
environmental impact studies. Downscaling methods to assess the effect of large-scale circulations on local parameters have. Statistical downscaling methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical downscaling methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).
Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics.
Chen, Wenan; Larrabee, Beth R; Ovsyannikova, Inna G; Kennedy, Richard B; Haralambieva, Iana H; Poland, Gregory A; Schaid, Daniel J
2015-07-01
Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf. PMID:25948564
A method for determining the weak statistical stationarity of a random process
NASA Technical Reports Server (NTRS)
Sadeh, W. Z.; Koper, C. A., Jr.
1978-01-01
A method for determining the weak statistical stationarity of a random process is presented. The core of this testing procedure consists of generating an equivalent ensemble which approximates a true ensemble. Formation of an equivalent ensemble is accomplished through segmenting a sufficiently long time history of a random process into equal, finite, and statistically independent sample records. The weak statistical stationarity is ascertained based on the time invariance of the equivalent-ensemble averages. Comparison of these averages with their corresponding time averages over a single sample record leads to a heuristic estimate of the ergodicity of a random process. Specific variance tests are introduced for evaluating the statistical independence of the sample records, the time invariance of the equivalent-ensemble autocorrelations, and the ergodicity. Examination and substantiation of these procedures were conducted utilizing turbulent velocity signals.
Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van't
2012-03-15
Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.
Vidal-Codina, F.; Nguyen, N.C.; Giles, M.B.; Peraire, J.
2015-09-15
We present a model and variance reduction method for the fast and reliable computation of statistical outputs of stochastic elliptic partial differential equations. Our method consists of three main ingredients: (1) the hybridizable discontinuous Galerkin (HDG) discretization of elliptic partial differential equations (PDEs), which allows us to obtain high-order accurate solutions of the governing PDE; (2) the reduced basis method for a new HDG discretization of the underlying PDE to enable real-time solution of the parameterized PDE in the presence of stochastic parameters; and (3) a multilevel variance reduction method that exploits the statistical correlation among the different reduced basis approximations and the high-fidelity HDG discretization to accelerate the convergence of the Monte Carlo simulations. The multilevel variance reduction method provides efficient computation of the statistical outputs by shifting most of the computational burden from the high-fidelity HDG approximation to the reduced basis approximations. Furthermore, we develop a posteriori error estimates for our approximations of the statistical outputs. Based on these error estimates, we propose an algorithm for optimally choosing both the dimensions of the reduced basis approximations and the sizes of Monte Carlo samples to achieve a given error tolerance. We provide numerical examples to demonstrate the performance of the proposed method.
Yang, Yong-Hui; Zhou, Feng; Guo, Huai-Cheng; Sheng, Hu; Liu, Hui; Dao, Xu; He, Cheng-Jie
2010-11-01
Various multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA), factor analysis (FA), and principal component analysis (PCA) were used to explain the spatial and temporal patterns of surface water pollution in Lake Dianchi. The dataset, obtained during the period 2003-2007 from the Kunming Environmental Monitoring Center, consisted of 12 variables surveyed monthly at eight sites. The CA grouped the 12 months into two groups, August-September and the remainder, and divided the lake into two regions based on their different physicochemical properties and pollution levels. The DA showed the best results for data reduction and pattern recognition in both temporal and spatial analysis. It calculated four parameters (TEMP, pH, CODMn, and Chl-a) to 85.4% correct assignment in the temporal analysis and three parameters (BOD, NH₄+-N, and TN) to almost 71.7% correct assignment in spatial analysis of the two clusters. The FA/PCA applied to datasets of two special clusters of the lake calculated four factors for each region, capturing 72.5% and 62.5% of the total variance, respectively. Strong loadings included DO, BOD, TN, CODCr, CODMn, NH₄+-N, TP, and EC. In addition, box-whisker plots and GIS further facilitated and supported the multivariate analysis results. PMID:19936953
Ishiwata, H; Takeda, Y; Kawasaki, Y; Kubota, H; Yamada, T
1996-01-01
The official methods for 'readily oxidizable substances (ROS)' in propionic acid as a food additive were compared. The methods examined were those adopted in the Compendium of Food Additive Specifications (CFAS) by the Joint FAO-WHO Expert Committee on Food Additives, FAO, The Japanese Standards for Food Additives (JSFA) by the Ministry of Health and Welfare, Japan, and the Food Chemicals Codex (FCC) by the National Research Council, USA. The methods given in CFAS and JSFA are the same (potassium permanganate consumption). However, by this method, manganese (VII) in potassium permanganate was readily reduced to colourless manganese(II) with some substances contained in the propionic acid before reacting with aldehydes, which are generally considered as 'readily oxidizable substances', to form brown manganese (IV) oxide. The FCC method (bromine consumption) for 'ROS' could be recommended because it was able to obtain quantitative results of 'ROS', including aldehydes. PMID:8647299
Multi-Reader ROC studies with Split-Plot Designs: A Comparison of Statistical Methods
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
Velugubantla, S. P.; Burian, S. J.; Brown, M. J.; McKinnon, A. T.; McPherson, T. N.; Han, W. S.
2004-01-01
Mesoscale meteorological codes and transport and dispersion models are increasingly being applied in urban areas. Representing urban terrain characteristics in these models is critical for accurate predictions of air flow, heating and cooling, and airborne contaminant concentrations in cities. A key component of urban terrain characterization is the description of building morphology (e.g., height, plan area, frontal area) and derived properties (e.g., roughness length). Methods to determine building morphological statistics range from manual field surveys to automated processing of digital building databases. In order to improve the quality and consistency of mesoscale meteorological and atmospheric dispersion modeling, a national dataset of building morphological statistics is needed. Currently, due to the expense and logistics of conducting detailed field surveys, building statistics have been derived for only small sections of a few cities. In most other cities, modeling projects rely on building statistics estimated using intuition and best guesses. There has been increasing emphasis in recent years to derive building statistics using digital building data or other data sources as a proxy for those data. Although there is a current expansion in public and private sector development of digital building data, at present there is insufficient data to derive a national building statistics database using automated analysis tools. Too many cities lack digital data on building footprints and heights and many of the cities having such data do so for only small areas. Due to the lack of sufficient digital building data, other datasets are used to estimate building statistics. Land use often serves as means to provide building statistics for a model domain, but the strength and consistency of the relationship between land use and building morphology is largely uncertain. In this paper, we investigate whether building statistics can be correlated to the underlying land
NASA Astrophysics Data System (ADS)
Schepen, Andrew; Wang, Q. J.
2015-03-01
The Australian Bureau of Meteorology produces statistical and dynamic seasonal streamflow forecasts. The statistical and dynamic forecasts are similarly reliable in ensemble spread; however, skill varies by catchment and season. Therefore, it may be possible to optimize forecasting skill by weighting and merging statistical and dynamic forecasts. Two model averaging methods are evaluated for merging forecasts for 12 locations. The first method, Bayesian model averaging (BMA), applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. The second method, quantile model averaging (QMA), applies averaging to forecast variable values (quantiles) for a given cumulative probability (quantile fraction). BMA and QMA are found to perform similarly in terms of overall skill scores and reliability in ensemble spread. Both methods improve forecast skill across catchments and seasons. However, when both the statistical and dynamical forecasting approaches are skillful but produce, on special occasions, very different event forecasts, the BMA merged forecasts for these events can have unusually wide and bimodal distributions. In contrast, the distributions of the QMA merged forecasts for these events are narrower, unimodal and generally more smoothly shaped, and are potentially more easily communicated to and interpreted by the forecast users. Such special occasions are found to be rare. However, every forecast counts in an operational service, and therefore the occasional contrast in merged forecasts between the two methods may be more significant than the indifference shown by the overall skill and reliability performance.
A study of two statistical methods as applied to shuttle solid rocket booster expenditures
NASA Technical Reports Server (NTRS)
Perlmutter, M.; Huang, Y.; Graves, M.
1974-01-01
The state probability technique and the Monte Carlo technique are applied to finding shuttle solid rocket booster expenditure statistics. For a given attrition rate per launch, the probable number of boosters needed for a given mission of 440 launches is calculated. Several cases are considered, including the elimination of the booster after a maximum of 20 consecutive launches. Also considered is the case where the booster is composed of replaceable components with independent attrition rates. A simple cost analysis is carried out to indicate the number of boosters to build initially, depending on booster costs. Two statistical methods were applied in the analysis: (1) state probability method which consists of defining an appropriate state space for the outcome of the random trials, and (2) model simulation method or the Monte Carlo technique. It was found that the model simulation method was easier to formulate while the state probability method required less computing time and was more accurate.
NASA Astrophysics Data System (ADS)
He, Tian; Ye, Wu; Pan, Qiang; Liu, Xiandong
2016-02-01
One key aspect of local fault diagnosis is how to effectively extract abrupt features from the vibration signals. This paper proposes a method to automatically extract abrupt information based on singular value decomposition and higher-order statistics. In order to observe the distribution law of singular values, a numerical analysis to simulate the noise, periodic signal, abrupt signal and singular value distribution is conducted. Based on higher-order statistics and spectrum analysis, a method to automatically choose the upper and lower borders of the singular value interval reflecting the abrupt information is built. And the selected singular values derived from this method are used to reconstruct abrupt signals. It is proven that the method is able to obtain accurate results by processing the rub-impact fault signal measured from the experiments. The analytical and experimental results indicate that the proposed method is feasible for automatically extracting abrupt information caused by faults like the rotor-stator rub-impact.
Statistical Physics Methods Provide the Exact Solution to a Long-Standing Problem of Genetics
NASA Astrophysics Data System (ADS)
Samal, Areejit; Martin, Olivier C.
2015-06-01
Analytic and computational methods developed within statistical physics have found applications in numerous disciplines. In this Letter, we use such methods to solve a long-standing problem in statistical genetics. The problem, posed by Haldane and Waddington [Genetics 16, 357 (1931)], concerns so-called recombinant inbred lines (RILs) produced by repeated inbreeding. Haldane and Waddington derived the probabilities of RILs when considering two and three genes but the case of four or more genes has remained elusive. Our solution uses two probabilistic frameworks relatively unknown outside of physics: Glauber's formula and self-consistent equations of the Schwinger-Dyson type. Surprisingly, this combination of statistical formalisms unveils the exact probabilities of RILs for any number of genes. Extensions of the framework may have applications in population genetics and beyond.
NASA Technical Reports Server (NTRS)
Benediktsson, Jon A.; Swain, Philip H.; Ersoy, Okan K.
1990-01-01
Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.
A similarity retrieval method for functional magnetic resonance imaging (fMRI) statistical maps
NASA Astrophysics Data System (ADS)
Tungaraza, R. F.; Guan, J.; Rolfe, S.; Atmosukarto, I.; Poliakov, A.; Kleinhans, N. M.; Aylward, E.; Ojemann, J.; Brinkley, J. F.; Shapiro, L. G.
2009-02-01
We propose a method for retrieving similar fMRI statistical images given a query fMRI statistical image. Our method thresholds the voxels within those images and extracts spatially distinct regions from the voxels that remain. Each region is defined by a feature vector that contains the region centroid, the region area, the average activation value for all the voxels within that region, the variance of those activation values, the average distance of each voxel within that region to the region's centroid, and the variance of the voxel's distance to the region's centroid. The similarity between two images is obtained by the summed minimum distance of their constituent feature vectors. Results on a dataset of fMRI statistical images from experiments involving distinct cognitive tasks are shown.
Statistical Physics Methods Provide the Exact Solution to a Long-Standing Problem of Genetics.
Samal, Areejit; Martin, Olivier C
2015-06-12
Analytic and computational methods developed within statistical physics have found applications in numerous disciplines. In this Letter, we use such methods to solve a long-standing problem in statistical genetics. The problem, posed by Haldane and Waddington [Genetics 16, 357 (1931)], concerns so-called recombinant inbred lines (RILs) produced by repeated inbreeding. Haldane and Waddington derived the probabilities of RILs when considering two and three genes but the case of four or more genes has remained elusive. Our solution uses two probabilistic frameworks relatively unknown outside of physics: Glauber's formula and self-consistent equations of the Schwinger-Dyson type. Surprisingly, this combination of statistical formalisms unveils the exact probabilities of RILs for any number of genes. Extensions of the framework may have applications in population genetics and beyond. PMID:26196831
A review of statistical methods for protein identification using tandem mass spectrometry
Serang, Oliver; Noble, William
2012-01-01
Tandem mass spectrometry has emerged as a powerful tool for the characterization of complex protein samples, an increasingly important problem in biology. The effort to efficiently and accurately perform inference on data from tandem mass spectrometry experiments has resulted in several statistical methods. We use a common framework to describe the predominant methods and discuss them in detail. These methods are classified using the following categories: set cover methods, iterative methods, and Bayesian methods. For each method, we analyze and evaluate the outcome and methodology of published comparisons to other methods; we use this comparison to comment on the qualities and weaknesses, as well as the overall utility, of all methods. We discuss the similarities between these methods and suggest directions for the field that would help unify these similar assumptions in a more rigorous manner and help enable efficient and reliable protein inference. PMID:22833779
ERIC Educational Resources Information Center
Ekmekci, Ozgur; Hancock, Adrienne B.; Swayze, Susan
2012-01-01
This paper examines the challenge of teaching statistical research methods in three master's degree programs at a private university based in Washington, DC. We, as three professors teaching at this university, discuss the way we employ innovative approaches to deal with this challenge. We ground our discussion within the theoretical framework of…
Taguchi statistical design and analysis of cleaning methods for spacecraft materials
NASA Technical Reports Server (NTRS)
Lin, Y.; Chung, S.; Kazarians, G. A.; Blosiu, J. O.; Beaudet, R. A.; Quigley, M. S.; Kern, R. G.
2003-01-01
In this study, we have extensively tested various cleaning protocols. The variant parameters included the type and concentration of solvent, type of wipe, pretreatment conditions, and various rinsing systems. Taguchi statistical method was used to design and evaluate various cleaning conditions on ten common spacecraft materials.
ERIC Educational Resources Information Center
Xu, Yonghong Jade; Meyer, Katrina A.; Morgan, Dianne D.
2009-01-01
This study used a mixed-methods approach to evaluate a hybrid teaching format that incorporated an online tutoring system, ALEKS, to address students' learning needs in a graduate-level introductory statistics course. Student performance in the hybrid course with ALEKS was found to be no different from that in a course taught in a traditional…
ERIC Educational Resources Information Center
Pliske, Rebecca M.; Caldwell, Tracy L.; Calin-Jageman, Robert J.; Taylor-Ritzler, Tina
2015-01-01
We developed a two-semester series of intensive (six-contact hours per week) behavioral research methods courses with an integrated statistics curriculum. Our approach includes the use of team-based learning, authentic projects, and Excel and SPSS. We assessed the effectiveness of our approach by examining our students' content area scores on the…
A REVIEW OF STATISTICAL METHODS FOR THE METEOROLOGICAL ADJUSTMENT OF TROPOSPHERIC OZONE. (R825173)
A variety of statistical methods for meteorological adjustment of ozone have been proposed in the literature over the last decade for purposes of forecasting, estimating ozone time trends, or investigating underlying mechanisms from an empirical perspective. T...
Diagnosing Skills of Statistical Hypothesis Testing Using the Rule Space Method
ERIC Educational Resources Information Center
Im, Seongah; Yin, Yue
2009-01-01
This study illustrated the use of the Rule Space Method to diagnose students' proficiencies in, skills and knowledge of statistical hypothesis testing. Participants included 96 undergraduate and, graduate students, of whom 94 were classified into one or more of the knowledge states identified by, the rule space analysis. Analysis at the level of…
Cooperative Learning in Virtual Environments: The Jigsaw Method in Statistical Courses
ERIC Educational Resources Information Center
Vargas-Vargas, Manuel; Mondejar-Jimenez, Jose; Santamaria, Maria-Letica Meseguer; Alfaro-Navarro, Jose-Luis; Fernandez-Aviles, Gema
2011-01-01
This document sets out a novel teaching methodology as used in subjects with statistical content, traditionally regarded by students as "difficult". In a virtual learning environment, instructional techniques little used in mathematical courses were employed, such as the Jigsaw cooperative learning method, which had to be adapted to the…
Interpreting Statistical Significance Test Results: A Proposed New "What If" Method.
ERIC Educational Resources Information Center
Kieffer, Kevin M.; Thompson, Bruce
As the 1994 publication manual of the American Psychological Association emphasized, "p" values are affected by sample size. As a result, it can be helpful to interpret the results of statistical significant tests in a sample size context by conducting so-called "what if" analyses. However, these methods can be inaccurate unless "corrected" effect…
Dexter, F; Macario, A; Traub, R D
2000-02-01
We present a statistical method that uses data from surgical services information systems to determine the minimum number of anesthetists to be scheduled for weekend call in an operating room suite. The staffing coverage is predicted that provides for sufficient anesthetists to cover each hour of a 24-hour weekend period, while satisfying a specified risk for being understaffed. The statistical method incorporates shifts of varying start times and durations, as well as historical weekend operating room caseload data. By using this method to schedule weekend staff, an anesthesia group can assure as few anesthetists are on call as possible, and for as few hours as possible, while maintaining the level of risk of understaffing that the anesthesia group is willing to accept. An anesthesia group also can use the method to calculate its risk of being understaffed in the surgical suite based on its existing weekend staffing plan. PMID:10876448
a Probability-Based Statistical Method to Extract Water Body of TM Images with Missing Information
NASA Astrophysics Data System (ADS)
Lian, Shizhong; Chen, Jiangping; Luo, Minghai
2016-06-01
Water information cannot be accurately extracted using TM images because true information is lost in some images because of blocking clouds and missing data stripes, thereby water information cannot be accurately extracted. Water is continuously distributed in natural conditions; thus, this paper proposed a new method of water body extraction based on probability statistics to improve the accuracy of water information extraction of TM images with missing information. Different disturbing information of clouds and missing data stripes are simulated. Water information is extracted using global histogram matching, local histogram matching, and the probability-based statistical method in the simulated images. Experiments show that smaller Areal Error and higher Boundary Recall can be obtained using this method compared with the conventional methods.
Nakae, Ken; Ikegaya, Yuji; Ishikawa, Tomoe; Oba, Shigeyuki; Urakubo, Hidetoshi; Koyama, Masanori; Ishii, Shin
2014-01-01
Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron–glia network. We attempted to identify neuron–glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron–glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron–glia systems. PMID:25393874
NASA Astrophysics Data System (ADS)
Clerc, F.; Njiki-Menga, G.-H.; Witschger, O.
2013-04-01
Most of the measurement strategies that are suggested at the international level to assess workplace exposure to nanomaterials rely on devices measuring, in real time, airborne particles concentrations (according different metrics). Since none of the instruments to measure aerosols can distinguish a particle of interest to the background aerosol, the statistical analysis of time resolved data requires special attention. So far, very few approaches have been used for statistical analysis in the literature. This ranges from simple qualitative analysis of graphs to the implementation of more complex statistical models. To date, there is still no consensus on a particular approach and the current period is always looking for an appropriate and robust method. In this context, this exploratory study investigates a statistical method to analyse time resolved data based on a Bayesian probabilistic approach. To investigate and illustrate the use of the this statistical method, particle number concentration data from a workplace study that investigated the potential for exposure via inhalation from cleanout operations by sandpapering of a reactor producing nanocomposite thin films have been used. In this workplace study, the background issue has been addressed through the near-field and far-field approaches and several size integrated and time resolved devices have been used. The analysis of the results presented here focuses only on data obtained with two handheld condensation particle counters. While one was measuring at the source of the released particles, the other one was measuring in parallel far-field. The Bayesian probabilistic approach allows a probabilistic modelling of data series, and the observed task is modelled in the form of probability distributions. The probability distributions issuing from time resolved data obtained at the source can be compared with the probability distributions issuing from the time resolved data obtained far-field, leading in a
Hahne, Florian; Arlt, Dorit; Sauermann, Mamatha; Majety, Meher; Poustka, Annemarie; Wiemann, Stefan; Huber, Wolfgang
2006-01-01
Highthroughput cell-based assays with flow cytometric readout provide a powerful technique for identifying components of biologic pathways and their interactors. Interpretation of these large datasets requires effective computational methods. We present a new approach that includes data pre-processing, visualization, quality assessment, and statistical inference. The software is freely available in the Bioconductor package prada. The method permits analysis of large screens to detect the effects of molecular interventions in cellular systems. PMID:16916453
NASA Astrophysics Data System (ADS)
Eum, H. I.; Cannon, A. J.
2015-12-01
Climate models are a key provider to investigate impacts of projected future climate conditions on regional hydrologic systems. However, there is a considerable mismatch of spatial resolution between GCMs and regional applications, in particular a region characterized by complex terrain such as Korean peninsula. Therefore, a downscaling procedure is an essential to assess regional impacts of climate change. Numerous statistical downscaling methods have been used mainly due to the computational efficiency and simplicity. In this study, four statistical downscaling methods [Bias-Correction/Spatial Disaggregation (BCSD), Bias-Correction/Constructed Analogue (BCCA), Multivariate Adaptive Constructed Analogs (MACA), and Bias-Correction/Climate Imprint (BCCI)] are applied to downscale the latest Climate Forecast System Reanalysis data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. By split sampling scheme, all methods are calibrated with observational station data for 19 years from 1973 to 1991 are and tested for the recent 19 years from 1992 to 2010. To assess skill of the downscaling methods, we construct a comprehensive suite of performance metrics that measure an ability of reproducing temporal correlation, distribution, spatial correlation, and extreme events. In addition, we employ Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to identify robust statistical downscaling methods based on the performance metrics for each season. The results show that downscaling skill is considerably affected by the skill of CFSR and all methods lead to large improvements in representing all performance metrics. According to seasonal performance metrics evaluated, when TOPSIS is applied, MACA is identified as the most reliable and robust method for all variables and seasons. Note that such result is derived from CFSR output which is recognized as near perfect climate data in climate studies. Therefore, the
NASA Astrophysics Data System (ADS)
Jara Chavez, A. G.; Torres Vicencio, F. O.
2015-03-01
Non-uniformity noise, it was, it is, and it will probably be one of the most non-desired attached companion of the infrared focal plane array (IRFPA) data. We present a higher order filter where the key advantage is based in its capacity to estimates the detection parameters and thus to compensate it for fixed pattern noise, as an enhancement of Constant Statistics (CS) theory. This paper shows a technique to improve the convergence in accelerated way for CS (AACS: Acceleration Algorithm for Constant Statistics). The effectiveness of this method is demonstrated by using simulated infrared video sequences and several real infrared video sequences obtained using two infrared cameras.
A Statistical Method for Reconstructing the Core Location of an Extensive Air Shower
NASA Astrophysics Data System (ADS)
Hedayati Kh., H.; Moradi, A.; Emami, M.
2015-09-01
Conventional methods of reconstructing extensive air showers (EASs) depend on a lateral density function which itself depends on shower size, age parameter, and core location. In the fitting procedure of a lateral density function to surface array information, the only parameter whose initial value is essential is core location. In this paper, we describe a refined version of a statistical method which can be used to find the initial trial core location of EASs with better precision than the conventional methods. In this method, we use arrival time information of secondary particles for finding not only arrival direction, but also core location.
Bishop, Joseph E.; Strack, O. E.
2011-03-22
A novel method is presented for assessing the convergence of a sequence of statistical distributions generated by direct Monte Carlo sampling. The primary application is to assess the mesh or grid convergence, and possibly divergence, of stochastic outputs from non-linear continuum systems. Example systems include those from fluid or solid mechanics, particularly those with instabilities and sensitive dependence on initial conditions or system parameters. The convergence assessment is based on demonstrating empirically that a sequence of cumulative distribution functions converges in the Linfty norm. The effect of finite sample sizes is quantified using confidence levels from the Kolmogorov–Smirnov statistic. The statistical method is independent of the underlying distributions. The statistical method is demonstrated using two examples: (1) the logistic map in the chaotic regime, and (2) a fragmenting ductile ring modeled with an explicit-dynamics finite element code. In the fragmenting ring example the convergence of the distribution describing neck spacing is investigated. The initial yield strength is treated as a random field. Two different random fields are considered, one with spatial correlation and the other without. Both cases converged, albeit to different distributions. The case with spatial correlation exhibited a significantly higher convergence rate compared with the one without spatial correlation.
Novel Method of Interconnect Worstcase Establishment with Statistically-Based Approaches
NASA Astrophysics Data System (ADS)
Jung, Won-Young; Kim, Hyungon; Kim, Yong-Ju; Wee, Jae-Kyung
In order for the interconnect effects due to process-induced variations to be applied to the designs in 0.13μm and below, it is necessary to determine and characterize the realistic interconnect worstcase models with high accuracy and speed. This paper proposes new statistically-based approaches to the characterization of realistic interconnect worstcase models which take into account process-induced variations. The Effective Common Geometry (ECG) and Accumulated Maximum Probability (AMP) algorithms have been developed and implemented into the new statistical interconnect worstcase design environment. To verify this statistical interconnect worstcase design environment, the 31-stage ring oscillators are fabricated and measured with UMC 0.13μm Logic process. The 15-stage ring oscillators are fabricated and measured with 0.18μm standard CMOS process for investigating its flexibility in other technologies. The results show that the relative errors of the new method are less than 1.00%, which is two times more accurate than the conventional worstcase method. Furthermore, the new interconnect worstcase design environment improves optimization speed by 29.61-32.01% compared to that of the conventional worstcase optimization. The new statistical interconnect worstcase design environment accurately predicts the worstcase and bestcase corners of non-normal distribution where conventional methods cannot do well.
40 CFR 80.8 - Sampling methods for gasoline, diesel fuel, fuel additives, and renewable fuels.
Code of Federal Regulations, 2014 CFR
2014-07-01
... of the Federal Register under 5 U.S.C. 552(a) and 1 CFR part 51. To enforce any edition other than... 40 Protection of Environment 17 2014-07-01 2014-07-01 false Sampling methods for gasoline, diesel... Provisions § 80.8 Sampling methods for gasoline, diesel fuel, fuel additives, and renewable fuels....
Damage diagnosis for SHM of existing civil structure with statistical diagnostic method
NASA Astrophysics Data System (ADS)
Iwasaki, Atsushi; Todoroki, Akira; Sugiya, Tsuneya; Sakai, Shinsuke
2004-07-01
The present research proposes a new automatic damage diagnostic method that does not require data of damaged state. Structural health monitoring is a noticeable technology for civil structures. Multiple damage diagnostic method for has been proposed, and most of them employ parametric method based on modeling or non-parametric method such as artificial neural networks. These methods demand much costs, and first of all, it is impossible to obtain data for training of damaged existing structures. That causes importance of development of the method, which diagnoses damage just from data of the intact state structure for existing structures. Therefore we purpose new statistical diagnostic method for structural damage detection. In the present method, system identification using a response surface is performed and damage is diagnosed by testing the change of this identified system by statistical test. The new method requires data of non-damaged state and does not require the complicated modeling and data of damaged state structure. As an example, the present study deals damage diagnosis of a jet-fan which installed to a tunnel on an expressway as a ventilator fan. Damages are detected from load of turnbuckles. As a result, the damage is successfully diagnosed with the method.
Application of multivariate statistical methods to the analysis of ancient Turkish potsherds
Martin, R.C.
1986-01-01
Three hundred ancient Turkish potsherds were analyzed by instrumental neutron activation analysis, and the resulting data analyzed by several techniques of multivariate statistical analysis, some only recently developed. The programs AGCLUS, MASLOC, and SIMCA were sequentially employed to characterize and group the samples by type of pottery and site of excavation. Comparison of the statistical analyses by each method provided archaeological insight into the site/type relationships of the samples and ultimately evidence relevant to the commercial relations between the ancient communities and specialization of pottery production over time. The techniques used for statistical analysis were found to be of significant potential utility in the future analysis of other archaeometric data sets. 25 refs., 33 figs.
2013-01-01
Background Abattoir condemnation data show promise as a rich source of data for syndromic surveillance of both animal and zoonotic diseases. However, inherent characteristics of abattoir condemnation data can bias results from space-time cluster detection methods for disease surveillance, and may need to be accounted for using various adjustment methods. The objective of this study was to compare the space-time scan statistics with different abilities to control for covariates and to assess their suitability for food animal syndromic surveillance. Four space-time scan statistic models were used including: animal class adjusted Poisson, space-time permutation, multi-level model adjusted Poisson, and a weighted normal scan statistic using model residuals. The scan statistics were applied to monthly bovine pneumonic lung and “parasitic liver” condemnation data from Ontario provincial abattoirs from 2001–2007. Results The number and space-time characteristics of identified clusters often varied between space-time scan tests for both “parasitic liver” and pneumonic lung condemnation data. While there were some similarities between isolated clusters in space, time and/or space-time, overall the results from space-time scan statistics differed substantially depending on the covariate adjustment approach used. Conclusions Variability in results among methods suggests that caution should be used in selecting space-time scan methods for abattoir surveillance. Furthermore, validation of different approaches with simulated or real outbreaks is required before conclusive decisions can be made concerning the best approach for conducting surveillance with these data. PMID:24246040