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
Mathematical and statistical analysis
NASA Technical Reports Server (NTRS)
Houston, A. Glen
1988-01-01
The goal of the mathematical and statistical analysis component of RICIS is to research, develop, and evaluate mathematical and statistical techniques for aerospace technology applications. Specific research areas of interest include modeling, simulation, experiment design, reliability assessment, and numerical analysis.
Deconstructing Statistical Analysis
ERIC Educational Resources Information Center
Snell, Joel
2014-01-01
Using a very complex statistical analysis and research method for the sake of enhancing the prestige of an article or making a new product or service legitimate needs to be monitored and questioned for accuracy. 1) The more complicated the statistical analysis, and research the fewer the number of learned readers can understand it. This adds a…
Statistical Energy Analysis Program
NASA Technical Reports Server (NTRS)
Ferebee, R. C.; Trudell, R. W.; Yano, L. I.; Nygaard, S. I.
1985-01-01
Statistical Energy Analysis (SEA) is powerful tool for estimating highfrequency vibration spectra of complex structural systems and incorporated into computer program. Basic SEA analysis procedure divided into three steps: Idealization, parameter generation, and problem solution. SEA computer program written in FORTRAN V for batch execution.
Statistical log analysis made practical
Mitchell, W.K.; Nelson, R.J. )
1991-06-01
This paper discusses the advantages of a statistical approach to log analysis. Statistical techniques use inverse methods to calculate formation parameters. The use of statistical techniques has been limited, however, by the complexity of the mathematics and lengthy computer time required to minimize traditionally used nonlinear equations.
Hahn, A.A.
1994-11-01
The complexity of instrumentation sometimes requires data analysis to be done before the result is presented to the control room. This tutorial reviews some of the theoretical assumptions underlying the more popular forms of data analysis and presents simple examples to illuminate the advantages and hazards of different techniques.
Analysis of network statistics
NASA Astrophysics Data System (ADS)
Cottrell, R. L. A.
1987-08-01
This talk discusses the types and sources of data obtainable from networks of computer systems and terminals connected by communications paths. These paths often utilize mixtures of protocols and devices (such as modems, multiplexors, switches and front-ends) from multiple vendors. The talk describes how the data can be gathered from these devices and protocol layers, consolidated, stored, and analyzed. The analysis typically includes merging information from data bases describing the network topology, components, etc. Examples of reports and displays of the information gleaned are shown, together with illustrations of how the information may be useful for troubleshooting, performance measurement, auditing, accounting, and trend prediction.
STATISTICAL SAMPLING AND DATA ANALYSIS
Research is being conducted to develop approaches to improve soil and sediment sampling techniques, measurement design and geostatistics, and data analysis via chemometric, environmetric, and robust statistical methods. Improvements in sampling contaminated soil and other hetero...
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.
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.
STATISTICS AND DATA ANALYSIS WORKSHOP
On Janauary 15 and 16, 2003, a workshop for Tribal water resources staff on Statistics and Data Analysis was held at the Indian Springs Lodge on the Forest County Potowatomi Reservation near Wabeno, WI. The workshop was co-sponsored by the EPA, Sokaogon Chippewa (Mole Lake) Comm...
Statistical Analysis in Climate Research
NASA Astrophysics Data System (ADS)
von Storch, Hans; Zwiers, Francis W.
2002-03-01
The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques, and the specialized techniques used specifically by climatologists are all contained within this one source. There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research.
Tools for Basic Statistical Analysis
NASA Technical Reports Server (NTRS)
Luz, Paul L.
2005-01-01
Statistical Analysis Toolset is a collection of eight Microsoft Excel spreadsheet programs, each of which performs calculations pertaining to an aspect of statistical analysis. These programs present input and output data in user-friendly, menu-driven formats, with automatic execution. The following types of calculations are performed: Descriptive statistics are computed for a set of data x(i) (i = 1, 2, 3 . . . ) entered by the user. Normal Distribution Estimates will calculate the statistical value that corresponds to cumulative probability values, given a sample mean and standard deviation of the normal distribution. Normal Distribution from two Data Points will extend and generate a cumulative normal distribution for the user, given two data points and their associated probability values. Two programs perform two-way analysis of variance (ANOVA) with no replication or generalized ANOVA for two factors with four levels and three repetitions. Linear Regression-ANOVA will curvefit data to the linear equation y=f(x) and will do an ANOVA to check its significance.
Performance of statistical energy analysis
NASA Technical Reports Server (NTRS)
Davis, R. F.; Hines, D. E.
1973-01-01
Statistical energy analysis (SEA) methods have been developed for high frequency modal analyses on random vibration environments. These SEA methods are evaluated by comparing analytical predictions to test results. Simple test methods are developed for establishing SEA parameter values. Techniques are presented, based on the comparison of the predictions with test values, for estimating SEA accuracy as a function of frequency for a general structure.
Statistical Analysis of RNA Backbone
Hershkovitz, Eli; Sapiro, Guillermo; Tannenbaum, Allen; Williams, Loren Dean
2009-01-01
Local conformation is an important determinant of RNA catalysis and binding. The analysis of RNA conformation is particularly difficult due to the large number of degrees of freedom (torsion angles) per residue. Proteins, by comparison, have many fewer degrees of freedom per residue. In this work, we use and extend classical tools from statistics and signal processing to search for clusters in RNA conformational space. Results are reported both for scalar analysis, where each torsion angle is separately studied, and for vectorial analysis, where several angles are simultaneously clustered. Adapting techniques from vector quantization and clustering to the RNA structure, we find torsion angle clusters and RNA conformational motifs. We validate the technique using well-known conformational motifs, showing that the simultaneous study of the total torsion angle space leads to results consistent with known motifs reported in the literature and also to the finding of new ones. PMID:17048391
Statistical analysis of nucleotide sequences.
Stückle, E E; Emmrich, C; Grob, U; Nielsen, P J
1990-01-01
In order to scan nucleic acid databases for potentially relevant but as yet unknown signals, we have developed an improved statistical model for pattern analysis of nucleic acid sequences by modifying previous methods based on Markov chains. We demonstrate the importance of selecting the appropriate parameters in order for the method to function at all. The model allows the simultaneous analysis of several short sequences with unequal base frequencies and Markov order k not equal to 0 as is usually the case in databases. As a test of these modifications, we show that in E. coli sequences there is a bias against palindromic hexamers which correspond to known restriction enzyme recognition sites. PMID:2251125
Statistical analysis of pyroshock data
NASA Astrophysics Data System (ADS)
Hughes, William O.
2002-05-01
The sample size of aerospace pyroshock test data is typically small. This often forces the engineer to make assumptions on its population distribution and to use conservative margins or methodologies in determining shock specifications. For example, the maximum expected environment is often derived by adding 3-6 dB to the maximum envelope of a limited amount of shock data. The recent availability of a large amount of pyroshock test data has allowed a rare statistical analysis to be performed. Findings and procedures from this analysis will be explained, including information on population distributions, procedures to properly combine families of test data, and methods of deriving appropriate shock specifications for a multipoint shock source.
Statistical Analysis of Tsunami Variability
NASA Astrophysics Data System (ADS)
Zolezzi, Francesca; Del Giudice, Tania; Traverso, Chiara; Valfrè, Giulio; Poggi, Pamela; Parker, Eric J.
2010-05-01
similar to that seen in ground motion attenuation correlations used for seismic hazard assessment. The second issue was intra-event variability. This refers to the differences in tsunami wave run-up along a section of coast during a single event. Intra-event variability investigated directly considering field observations. The tsunami events used in the statistical evaluation were selected on the basis of the completeness and reliability of the available data. Tsunami considered for the analysis included the recent and well surveyed tsunami of Boxing Day 2004 (Great Indian Ocean Tsunami), Java 2006, Okushiri 1993, Kocaeli 1999, Messina 1908 and a case study of several historic events in Hawaii. Basic statistical analysis was performed on the field observations from these tsunamis. For events with very wide survey regions, the run-up heights have been grouped in order to maintain a homogeneous distance from the source. Where more than one survey was available for a given event, the original datasets were maintained separately to avoid combination of non-homogeneous data. The observed run-up measurements were used to evaluate the minimum, maximum, average, standard deviation and coefficient of variation for each data set. The minimum coefficient of variation was 0.12 measured for the 2004 Boxing Day tsunami at Nias Island (7 data) while the maximum is 0.98 for the Okushiri 1993 event (93 data). The average coefficient of variation is of the order of 0.45.
Asymptotic modal analysis and statistical energy analysis
NASA Technical Reports Server (NTRS)
Dowell, Earl H.
1992-01-01
Asymptotic Modal Analysis (AMA) is a method which is used to model linear dynamical systems with many participating modes. The AMA method was originally developed to show the relationship between statistical energy analysis (SEA) and classical modal analysis (CMA). In the limit of a large number of modes of a vibrating system, the classical modal analysis result can be shown to be equivalent to the statistical energy analysis result. As the CMA result evolves into the SEA result, a number of systematic assumptions are made. Most of these assumptions are based upon the supposition that the number of modes approaches infinity. It is for this reason that the term 'asymptotic' is used. AMA is the asymptotic result of taking the limit of CMA as the number of modes approaches infinity. AMA refers to any of the intermediate results between CMA and SEA, as well as the SEA result which is derived from CMA. The main advantage of the AMA method is that individual modal characteristics are not required in the model or computations. By contrast, CMA requires that each modal parameter be evaluated at each frequency. In the latter, contributions from each mode are computed and the final answer is obtained by summing over all the modes in the particular band of interest. AMA evaluates modal parameters only at their center frequency and does not sum the individual contributions from each mode in order to obtain a final result. The method is similar to SEA in this respect. However, SEA is only capable of obtaining spatial averages or means, as it is a statistical method. Since AMA is systematically derived from CMA, it can obtain local spatial information as well.
Asymptotic modal analysis and statistical energy analysis
NASA Technical Reports Server (NTRS)
Dowell, Earl H.; Peretti, Linda F.
1990-01-01
The sound field of a structural-acoustic enclosure was subject to experimental analysis and theoretical description in order to develop an efficient and accurate method for predicting sound pressure levels in enclosures such as aircraft fuselages. Asymptotic Modal Analysis (AMA) is the method under investigation. AMA is derived from classical modal analysis (CMA) by considering the asymptotic limit of the sound pressure level as the number of acoustic and/or structural modes approaches infinity. Using AMA, results identical to those of Statistical Energy Analysis (SEA) were obtained for the spatially-averaged sound pressure levels in the interior. AMA is systematically derived from CMA and therefore the degree of generality of the end result can be adjusted through the choice of appropriate simplifying assumptions. For example, AMA can be used to obtain local sound pressure levels at particular points inside the enclosure, or to include the effects of varying the size and/or location of the sound source. AMA theoretical results were compared with CMA theory and also with experiment for the case where the structural-acoustic enclosure is a rectangular cavity with part of one wall flexible and vibrating, while the rest of the cavity is rigid.
Statistical analysis of barefoot impressions.
Kennedy, Robert B; Pressman, Irwin S; Chen, Sanping; Petersen, Peter H; Pressman, Ari E
2003-01-01
Comparison of the shapes of barefoot impressions from an individual with footprints or shoes linked to a crime may be useful as a means of including or excluding that individual as possibly being at the scene of a crime. The question of the distinguishability of a person's barefoot print arises frequently. This study indicates that measurements taken from the outlines of inked footprint impressions show a great degree of variability between donors and a great degree of similarity for multiple impressions taken from the same donor. The normality of the set of measurements on footprint outlines that we have selected for this study is confirmed. A statistical justification for the use of the product rule on individual statistical precisions is developed. PMID:12570199
Statistical analysis of planetary surfaces
NASA Astrophysics Data System (ADS)
Schmidt, Frederic; Landais, Francois; Lovejoy, Shaun
2015-04-01
In the last decades, a huge amount of topographic data has been obtained by several techniques (laser and radar altimetry, DTM…) for different bodies in the solar system, including Earth, Mars, the Moon etc.. In each case, topographic fields exhibit an extremely high variability with details at each scale, from millimeter to thousands of kilometers. This complexity seems to prohibit global descriptions or global topography models. Nevertheless, this topographic complexity is well-known to exhibit scaling laws that establish a similarity between scales and permit simpler descriptions and models. Indeed, efficient simulations can be made using the statistical properties of scaling fields (fractals). But realistic simulations of global topographic fields must be multi (not mono) scaling behaviour, reflecting the extreme variability and intermittency observed in real fields that can not be generated by simple scaling models. A multiscaling theory has been developed in order to model high variability and intermittency. This theory is a good statistical candidate to model the topography field with a limited number of parameters (called the multifractal parameters). In our study, we show that statistical properties of the Martian topography is accurately reproduced by this model, leading to new interpretation of geomorphological processes.
Asymptotic modal analysis and statistical energy analysis
NASA Technical Reports Server (NTRS)
Dowell, Earl H.
1988-01-01
Statistical Energy Analysis (SEA) is defined by considering the asymptotic limit of Classical Modal Analysis, an approach called Asymptotic Modal Analysis (AMA). The general approach is described for both structural and acoustical systems. The theoretical foundation is presented for structural systems, and experimental verification is presented for a structural plate responding to a random force. Work accomplished subsequent to the grant initiation focusses on the acoustic response of an interior cavity (i.e., an aircraft or spacecraft fuselage) with a portion of the wall vibrating in a large number of structural modes. First results were presented at the ASME Winter Annual Meeting in December, 1987, and accepted for publication in the Journal of Vibration, Acoustics, Stress and Reliability in Design. It is shown that asymptotically as the number of acoustic modes excited becomes large, the pressure level in the cavity becomes uniform except at the cavity boundaries. However, the mean square pressure at the cavity corner, edge and wall is, respectively, 8, 4, and 2 times the value in the cavity interior. Also it is shown that when the portion of the wall which is vibrating is near a cavity corner or edge, the response is significantly higher.
Statistical Power in Meta-Analysis
ERIC Educational Resources Information Center
Liu, Jin
2015-01-01
Statistical power is important in a meta-analysis study, although few studies have examined the performance of simulated power in meta-analysis. The purpose of this study is to inform researchers about statistical power estimation on two sample mean difference test under different situations: (1) the discrepancy between the analytical power and…
Statistical Data Analysis in the Computer Age
NASA Astrophysics Data System (ADS)
Efron, Bradley; Tibshirani, Robert
1991-07-01
Most of our familiar statistical methods, such as hypothesis testing, linear regression, analysis of variance, and maximum likelihood estimation, were designed to be implemented on mechanical calculators. modern electronic computation has encouraged a host of new statistical methods that require fewer distributional assumptions than their predecessors and can be applied to more complicated statistical estimators. These methods allow the scientist to explore and describe data and draw valid statistical inferences without the usual concerns for mathematical tractability. This is possible because traditional methods of mathematical analysis are replaced by specially constructed computer algorithms. Mathematics has not disappeared from statistical theory. It is the main method for deciding which algorithms are correct and efficient tools for automating statistical inference.
Statistical Survey and Analysis Handbook.
ERIC Educational Resources Information Center
Smith, Kenneth F.
The National Food and Agriculture Council of the Philippines regularly requires rapid feedback data for analysis, which will assist in monitoring programs to improve and increase the production of selected crops by small scale farmers. Since many other development programs in various subject matter areas also require similar statistical…
Statistical Analysis of DWPF ARG-1 Data
Harris, S.P.
2001-03-02
A statistical analysis of analytical results for ARG-1, an Analytical Reference Glass, blanks, and the associated calibration and bench standards has been completed. These statistics provide a means for DWPF to review the performance of their laboratory as well as identify areas of improvement.
Explorations in Statistics: The Analysis of Change
ERIC Educational Resources Information Center
Curran-Everett, Douglas; Williams, Calvin L.
2015-01-01
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This tenth installment of "Explorations in Statistics" explores the analysis of a potential change in some physiological response. As researchers, we often express absolute change as percent change so we can…
Statistical Analysis For Nucleus/Nucleus Collisions
NASA Technical Reports Server (NTRS)
Mcguire, Stephen C.
1989-01-01
Report describes use of several statistical techniques to charactertize angular distributions of secondary particles emitted in collisions of atomic nuclei in energy range of 24 to 61 GeV per nucleon. Purpose of statistical analysis to determine correlations between intensities of emitted particles and angles comfirming existence of quark/gluon plasma.
STATISTICAL ANALYSIS OF A DETERMINISTIC STOCHASTIC ORBIT
Kaufman, Allan N.; Abarbanel, Henry D.I.; Grebogi, Celso
1980-05-01
If the solution of a deterministic equation is stochastic (in the sense of orbital instability), it can be subjected to a statistical analysis. This is illustrated for a coded orbit of the Chirikov mapping. Statistical dependence and the Markov assumption are tested. The Kolmogorov-Sinai entropy is related to the probability distribution for the orbit.
Statistical analysis of histopathological endpoints.
Green, John W; Springer, Timothy A; Saulnier, Amy N; Swintek, Joe
2014-05-01
Histopathological assessments of fish from aquatic ecotoxicology studies are being performed with increasing frequency. Aquatic ecotoxicology studies performed for submission to regulatory agencies are usually conducted with multiple subjects (e.g., fish) in each of multiple vessels (replicates) within a water control and within each of several concentrations of a test substance. A number of histopathological endpoints are evaluated in each fish, and a severity score is generally recorded for each endpoint. The severity scores are often recorded using a nonquantitative scale of 0 to 4, with 0 indicating no effect, 1 indicating minimal effect, through 4 for severe effect. Statistical methods often used to analyze these scores suffer from several shortcomings: computing average scores as though scores were quantitative values, considering only the frequency of abnormality while ignoring severity, ignoring any concentration-response trend, and ignoring the possible correlation between responses of individuals within test vessels. A new test, the Rao-Scott Cochran-Armitage by Slices (RSCABS), is proposed that incorporates the replicate vessel experimental design and the biological expectation that the severity of the effect tends to increase with increasing doses or concentrations, while retaining the individual subject scores and taking into account the severity as well as frequency of scores. A power simulation and examples demonstrate the performance of the test. R-based software has been developed to carry out this test and is available free of charge at www.epa.gov/med/Prods_Pubs/rscabs.htm. The SAS-based RSCABS software is available from the first and third authors. PMID:24464649
Advice on statistical analysis for Circulation Research.
Kusuoka, Hideo; Hoffman, Julien I E
2002-10-18
Since the late 1970s when many journals published articles warning about the misuse of statistical methods in the analysis of data, researchers have become more careful about statistical analysis, but errors including low statistical power and inadequate analysis of repeated-measurement studies are still prevalent. In this review, several statistical methods are introduced that are not always familiar to basic and clinical cardiologists but may be useful for revealing the correct answer from the data. The aim of this review is not only to draw the attention of investigators to these tests but also to stress the conditions in which they are applicable. These methods are now generally available in statistical program packages. Researchers need not know how to calculate the statistics from the data but are required to select the correct method from the menu and interpret the statistical results accurately. With the choice of appropriate statistical programs, the issue is no longer how to do the test but when to do it. PMID:12386142
A Statistical Analysis of Cotton Fiber Properties
NASA Astrophysics Data System (ADS)
Ghosh, Anindya; Das, Subhasis; Majumder, Asha
2016-04-01
This paper reports a statistical analysis of different cotton fiber properties, such as strength, breaking elongation, upper half mean length, length uniformity index, short fiber index, micronaire, reflectance and yellowness measured from 1200 cotton bales. The uni-variate, bi-variate and multi-variate statistical analysis have been invoked to elicit interrelationship between above-mentioned properties taking them up singularly, pairwise and multiple way, respectively. In multi-variate analysis all cotton fiber properties are simultaneously considered for multi-dimensional techniques of principal factor analysis.
Statistical Analysis Techniques for Small Sample Sizes
NASA Technical Reports Server (NTRS)
Navard, S. E.
1984-01-01
The small sample sizes problem which is encountered when dealing with analysis of space-flight data is examined. Because of such a amount of data available, careful analyses are essential to extract the maximum amount of information with acceptable accuracy. Statistical analysis of small samples is described. The background material necessary for understanding statistical hypothesis testing is outlined and the various tests which can be done on small samples are explained. Emphasis is on the underlying assumptions of each test and on considerations needed to choose the most appropriate test for a given type of analysis.
Statistical Tools for Forensic Analysis of Toolmarks
David Baldwin; Max Morris; Stan Bajic; Zhigang Zhou; James Kreiser
2004-04-22
Recovery and comparison of toolmarks, footprint impressions, and fractured surfaces connected to a crime scene are of great importance in forensic science. The purpose of this project is to provide statistical tools for the validation of the proposition that particular manufacturing processes produce marks on the work-product (or tool) that are substantially different from tool to tool. The approach to validation involves the collection of digital images of toolmarks produced by various tool manufacturing methods on produced work-products and the development of statistical methods for data reduction and analysis of the images. The developed statistical methods provide a means to objectively calculate a ''degree of association'' between matches of similarly produced toolmarks. The basis for statistical method development relies on ''discriminating criteria'' that examiners use to identify features and spatial relationships in their analysis of forensic samples. The developed data reduction algorithms utilize the same rules used by examiners for classification and association of toolmarks.
Statistical energy analysis computer program, user's guide
NASA Technical Reports Server (NTRS)
Trudell, R. W.; Yano, L. I.
1981-01-01
A high frequency random vibration analysis, (statistical energy analysis (SEA) method) is examined. The SEA method accomplishes high frequency prediction of arbitrary structural configurations. A general SEA computer program is described. A summary of SEA theory, example problems of SEA program application, and complete program listing are presented.
Multiset Statistics for Gene Set Analysis
Newton, Michael A.; Wang, Zhishi
2015-01-01
An important data analysis task in statistical genomics involves the integration of genome-wide gene-level measurements with preexisting data on the same genes. A wide variety of statistical methodologies and computational tools have been developed for this general task. We emphasize one particular distinction among methodologies, namely whether they process gene sets one at a time (uniset) or simultaneously via some multiset technique. Owing to the complexity of collections of gene sets, the multiset approach offers some advantages, as it naturally accommodates set-size variations and among-set overlaps. However, this approach presents both computational and inferential challenges. After reviewing some statistical issues that arise in uniset analysis, we examine two model-based multiset methods for gene list data. PMID:25914887
MICROARRAY DATA ANALYSIS USING MULTIPLE STATISTICAL MODELS
Microarray Data Analysis Using Multiple Statistical Models
Wenjun Bao1, Judith E. Schmid1, Amber K. Goetz1, Ming Ouyang2, William J. Welsh2,Andrew I. Brooks3,4, ChiYi Chu3,Mitsunori Ogihara3,4, Yinhe Cheng5, David J. Dix1. 1National Health and Environmental Effects Researc...
Statistical Analysis Experiment for Freshman Chemistry Lab.
ERIC Educational Resources Information Center
Salzsieder, John C.
1995-01-01
Describes a laboratory experiment dissolving zinc from galvanized nails in which data can be gathered very quickly for statistical analysis. The data have sufficient significant figures and the experiment yields a nice distribution of random errors. Freshman students can gain an appreciation of the relationships between random error, number of…
Bayesian Statistics for Biological Data: Pedigree Analysis
ERIC Educational Resources Information Center
Stanfield, William D.; Carlton, Matthew A.
2004-01-01
The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.
Applied Behavior Analysis and Statistical Process Control?
ERIC Educational Resources Information Center
Hopkins, B. L.
1995-01-01
Incorporating statistical process control (SPC) methods into applied behavior analysis is discussed. It is claimed that SPC methods would likely reduce applied behavior analysts' intimate contacts with problems and would likely yield poor treatment and research decisions. Cases and data presented by Pfadt and Wheeler (1995) are cited as examples.…
Statistical shape analysis: From landmarks to diffeomorphisms.
Zhang, Miaomiao; Golland, Polina
2016-10-01
We offer a blazingly brief review of evolution of shape analysis methods in medical imaging. As the representations and the statistical models grew more sophisticated, the problem of shape analysis has been gradually redefined to accept images rather than binary segmentations as a starting point. This transformation enabled shape analysis to take its rightful place in the arsenal of tools for extracting and understanding patterns in large clinical image sets. We speculate on the future developments in shape analysis and potential applications that would bring this mathematically rich area to bear on clinical practice. PMID:27377332
Unified statistical approach to cortical thickness analysis.
Chung, Moo K; Robbins, Steve; Evans, Alan C
2005-01-01
This paper presents a unified image processing and analysis framework for cortical thickness in characterizing a clinical population. The emphasis is placed on the development of data smoothing and analysis framework. The human brain cortex is a highly convoluted surface. Due to the convoluted non-Euclidean surface geometry, data smoothing and analysis on the cortex are inherently difficult. When measurements lie on a curved surface, it is natural to assign kernel smoothing weights based on the geodesic distance along the surface rather than the Euclidean distance. We present a new data smoothing framework that address this problem implicitly without actually computing the geodesic distance and present its statistical properties. Afterwards, the statistical inference is based on the random field theory based multiple comparison correction. As an illustration, we have applied the method in detecting the regions of abnormal cortical thickness in 16 high functioning autistic children. PMID:17354731
Statistical Analysis of Thermal Analysis Margin
NASA Technical Reports Server (NTRS)
Garrison, Matthew B.
2011-01-01
NASA Goddard Space Flight Center requires that each project demonstrate a minimum of 5 C margin between temperature predictions and hot and cold flight operational limits. The bounding temperature predictions include worst-case environment and thermal optical properties. The purpose of this work is to: assess how current missions are performing against their pre-launch bounding temperature predictions and suggest any possible changes to the thermal analysis margin rules
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.
Comparative statistical analysis of planetary surfaces
NASA Astrophysics Data System (ADS)
Schmidt, Frédéric; Landais, Francois; Lovejoy, Shaun
2016-04-01
In the present study, we aim to provide a statistical and comparative description of topographic fields by using the huge amount of topographic data available for different bodies in the solar system, including Earth, Mars, the Moon etc.. Our goal is to characterize and quantify the geophysical processes involved by a relevant statistical description. In each case, topographic fields exhibit an extremely high variability with details at each scale, from millimeter to thousands of kilometers. This complexity seems to prohibit global descriptions or global topography models. Nevertheless, this topographic complexity is well-known to exhibit scaling laws that establish a similarity between scales and permit simpler descriptions and models. Indeed, efficient simulations can be made using the statistical properties of scaling fields (fractals). But realistic simulations of global topographic fields must be multi (not mono) scaling behaviour, reflecting the extreme variability and intermittency observed in real fields that can not be generated by simple scaling models. A multiscaling theory has been developed in order to model high variability and intermittency. This theory is a good statistical candidate to model the topography field with a limited number of parameters (called the multifractal parameters). After a global analysis of Mars (Landais et. al, 2015) we have performed similar analysis on different body in the solar system including the Moon, Venus and mercury indicating that the mulifractal parameters might be relevant to explain the competition between several processes operating on multiple scales
Applied behavior analysis and statistical process control?
Hopkins, B L
1995-01-01
This paper examines Pfadt and Wheeler's (1995) suggestions that the methods of statistical process control (SPC) be incorporated into applied behavior analysis. The research strategies of SPC are examined and compared to those of applied behavior analysis. I argue that the statistical methods that are a part of SPC would likely reduce applied behavior analysts' intimate contacts with the problems with which they deal and would, therefore, likely yield poor treatment and research decisions. Examples of these kinds of results and decisions are drawn from the cases and data Pfadt and Wheeler present. This paper also describes and clarifies many common misconceptions about SPC, including W. Edwards Deming's involvement in its development, its relationship to total quality management, and its confusion with various other methods designed to detect sources of unwanted variability. PMID:7592156
Statistical Analysis of Iberian Peninsula Megaliths Orientations
NASA Astrophysics Data System (ADS)
González-García, A. C.
2009-08-01
Megalithic monuments have been intensively surveyed and studied from the archaeoastronomical point of view in the past decades. We have orientation measurements for over one thousand megalithic burial monuments in the Iberian Peninsula, from several different periods. These data, however, lack a sound understanding. A way to classify and start to understand such orientations is by means of statistical analysis of the data. A first attempt is done with simple statistical variables and a mere comparison between the different areas. In order to minimise the subjectivity in the process a further more complicated analysis is performed. Some interesting results linking the orientation and the geographical location will be presented. Finally I will present some models comparing the orientation of the megaliths in the Iberian Peninsula with the rising of the sun and the moon at several times of the year.
Multivariate analysis: A statistical approach for computations
NASA Astrophysics Data System (ADS)
Michu, Sachin; Kaushik, Vandana
2014-10-01
Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.
A statistical analysis of NMR spectrometer noise.
Grage, Halfdan; Akke, Mikael
2003-05-01
Estimation of NMR spectral parameters, using e.g. maximum likelihood methods, is commonly based on the assumption of white complex Gaussian noise in the signal obtained by quadrature detection. Here we present a statistical analysis with the purpose of discussing and testing the validity of this fundamental assumption. Theoretical expressions are derived for the correlation structure of the noise under various conditions, showing that in general the noise in the sampled signal is not strictly white, even if the thermal noise in the receiver steps prior to digitisation can be characterised as white Gaussian noise. It is shown that the noise correlation properties depend on the ratio between the sampling frequency and the filter cut-off frequency, as well as the filter characteristics. The theoretical analysis identifies conditions that are expected to yield non-white noise in the sampled signal. Extensive statistical characterisation of experimental noise confirms the theoretical predictions. The statistical methods outlined here are also useful for residual analysis in connection with validation of the model and the parameter estimates. PMID:12762994
Statistical Analysis of Spectra with Many Lines
NASA Astrophysics Data System (ADS)
van Dyk, D. A.; Kang, H. S.; Connors, A.; Kashyap, V. L.; Siemiginowska, A.
2001-12-01
Please join us in our wider effort to engage the strengths of modern computational statistics methods in solving challenging stellar and solar data analysis problems. As just one example (of a great breadth of possibilities) consider analyzing a spectrum with a very large number of lines. Some of these may be faint, merged, indistinguishable from each other and the underlying smooth continuum. The ensemble of line intensities follows a predictable distribution. The shape of this distribution depends on the properties of the source, e.g., its temperature, abundances, and emission measure. Hence, a better understanding of the distribution of line fluxes in a particular source may tighten our inference for other model parameters such as temperature---even when very few lines are actually easy to distinguish. To take advantage of this structure, we directly model the distribution of the line fluxes rather than fitting each line flux directly or ``investing'' the emissivities to get a DEM. Statistically, this strategy reduces the number of free parameters, which we expect will lead to improved statistical properties. We believe this method holds much promise for improved analysis, especially for low count sources. For example, we expect this method to correctly account for the ``pseudo-continuum'' that results from the large number of faint, unresolvable lines in X-ray grating spectra. Moreover, our statistical methods should apply directly to other settings involving a multitude of lines such as timing data. We hope that these methods will increase our statistical power to set the continuum level in the presence of a multitude of lines and to distinguish weak lines from fluctuations in the continuum. Funding for this project partially provided by NSF grant and DMS-01-04129 and by NASA Contract NAS8-39073 (CXC).
Common misconceptions about data analysis and statistics.
Motulsky, Harvey J
2015-02-01
Ideally, any experienced investigator with the right tools should be able to reproduce a finding published in a peer-reviewed biomedical science journal. In fact, the reproducibility of a large percentage of published findings has been questioned. Undoubtedly, there are many reasons for this, but one reason may be that investigators fool themselves due to a poor understanding of statistical concepts. In particular, investigators often make these mistakes: (1) P-Hacking. This is when you reanalyze a data set in many different ways, or perhaps reanalyze with additional replicates, until you get the result you want. (2) Overemphasis on P values rather than on the actual size of the observed effect. (3) Overuse of statistical hypothesis testing, and being seduced by the word "significant". (4) Overreliance on standard errors, which are often misunderstood. PMID:25692012
Common misconceptions about data analysis and statistics.
Motulsky, Harvey J
2014-10-01
Ideally, any experienced investigator with the right tools should be able to reproduce a finding published in a peer-reviewed biomedical science journal. In fact, however, the reproducibility of a large percentage of published findings has been questioned. Undoubtedly, there are many reasons for this, but one reason may be that investigators fool themselves due to a poor understanding of statistical concepts. In particular, investigators often make these mistakes: 1) P-hacking, which is when you reanalyze a data set in many different ways, or perhaps reanalyze with additional replicates, until you get the result you want; 2) overemphasis on P values rather than on the actual size of the observed effect; 3) overuse of statistical hypothesis testing, and being seduced by the word "significant"; and 4) over-reliance on standard errors, which are often misunderstood. PMID:25204545
Common misconceptions about data analysis and statistics.
Motulsky, Harvey J
2014-11-01
Ideally, any experienced investigator with the right tools should be able to reproduce a finding published in a peer-reviewed biomedical science journal. In fact, the reproducibility of a large percentage of published findings has been questioned. Undoubtedly, there are many reasons for this, but one reason maybe that investigators fool themselves due to a poor understanding of statistical concepts. In particular, investigators often make these mistakes: 1. P-Hacking. This is when you reanalyze a data set in many different ways, or perhaps reanalyze with additional replicates, until you get the result you want. 2. Overemphasis on P values rather than on the actual size of the observed effect. 3. Overuse of statistical hypothesis testing, and being seduced by the word "significant". 4. Overreliance on standard errors, which are often misunderstood. PMID:25213136
Statistical Tolerance and Clearance Analysis for Assembly
NASA Technical Reports Server (NTRS)
Lee, S.; Yi, C.
1996-01-01
Tolerance is inevitable because manufacturing exactly equal parts is known to be impossible. Furthermore, the specification of tolerances is an integral part of product design since tolerances directly affect the assemblability, functionality, manufacturability, and cost effectiveness of a product. In this paper, we present statistical tolerance and clearance analysis for the assembly. Our proposed work is expected to make the following contributions: (i) to help the designers to evaluate products for assemblability, (ii) to provide a new perspective to tolerance problems, and (iii) to provide a tolerance analysis tool which can be incorporated into a CAD or solid modeling system.
Statistical analysis in dBASE-compatible databases.
Hauer-Jensen, M
1991-01-01
Database management in clinical and experimental research often requires statistical analysis of the data in addition to the usual functions for storing, organizing, manipulating and reporting. With most database systems, transfer of data to a dedicated statistics package is a relatively simple task. However, many statistics programs lack the powerful features found in database management software. dBASE IV and compatible programs are currently among the most widely used database management programs. d4STAT is a utility program for dBASE, containing a collection of statistical functions and tests for data stored in the dBASE file format. By using d4STAT, statistical calculations may be performed directly on the data stored in the database without having to exit dBASE IV or export data. Record selection and variable transformations are performed in memory, thus obviating the need for creating new variables or data files. The current version of the program contains routines for descriptive statistics, paired and unpaired t-tests, correlation, linear regression, frequency tables, Mann-Whitney U-test, Wilcoxon signed rank test, a time-saving procedure for counting observations according to user specified selection criteria, survival analysis (product limit estimate analysis, log-rank test, and graphics), and normal t and chi-squared distribution functions. PMID:2004275
Statistical Uncertainty Analysis Applied to Criticality Calculation
Hartini, Entin; Andiwijayakusuma, Dinan; Susmikanti, Mike; Nursinta, A. W.
2010-06-22
In this paper, we present an uncertainty methodology based on a statistical approach, for assessing uncertainties in criticality prediction using monte carlo method due to uncertainties in the isotopic composition of the fuel. The methodology has been applied to criticality calculations with MCNP5 with additional stochastic input of the isotopic fuel composition. The stochastic input were generated using the latin hypercube sampling method based one the probability density function of each nuclide composition. The automatic passing of the stochastic input to the MCNP and the repeated criticality calculation is made possible by using a python script to link the MCNP and our latin hypercube sampling code.
Meaningful statistical analysis of large computational clusters.
Gentile, Ann C.; Marzouk, Youssef M.; Brandt, James M.; Pebay, Philippe Pierre
2005-07-01
Effective monitoring of large computational clusters demands the analysis of a vast amount of raw data from a large number of machines. The fundamental interactions of the system are not, however, well-defined, making it difficult to draw meaningful conclusions from this data, even if one were able to efficiently handle and process it. In this paper we show that computational clusters, because they are comprised of a large number of identical machines, behave in a statistically meaningful fashion. We therefore can employ normal statistical methods to derive information about individual systems and their environment and to detect problems sooner than with traditional mechanisms. We discuss design details necessary to use these methods on a large system in a timely and low-impact fashion.
Apparatus for statistical time-series analysis of electrical signals
NASA Technical Reports Server (NTRS)
Stewart, C. H. (Inventor)
1973-01-01
An apparatus for performing statistical time-series analysis of complex electrical signal waveforms, permitting prompt and accurate determination of statistical characteristics of the signal is presented.
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
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.
Statistical analysis of extreme auroral electrojet indices
NASA Astrophysics Data System (ADS)
Nakamura, Masao; Yoneda, Asato; Oda, Mitsunobu; Tsubouchi, Ken
2015-09-01
Extreme auroral electrojet activities can damage electrical power grids due to large induced currents in the Earth, degrade radio communications and navigation systems due to the ionospheric disturbances and cause polar-orbiting satellite anomalies due to the enhanced auroral electron precipitation. Statistical estimation of extreme auroral electrojet activities is an important factor in space weather research. For this estimation, we utilize extreme value theory (EVT), which focuses on the statistical behavior in the tail of a distribution. As a measure of auroral electrojet activities, auroral electrojet indices AL, AU, and AE, are used, which describe the maximum current strength of the westward and eastward auroral electrojets and the sum of the two oppositely directed in the auroral latitude ionosphere, respectively. We provide statistical evidence for finite upper limits to AL and AU and estimate the annual expected number and probable intensity of their extreme events. We detect two different types of extreme AE events; therefore, application of the appropriate EVT analysis to AE is difficult.
Statistical Hot Channel Analysis for the NBSR
Cuadra A.; Baek J.
2014-05-27
A statistical analysis of thermal limits has been carried out for the research reactor (NBSR) at the National Institute of Standards and Technology (NIST). The objective of this analysis was to update the uncertainties of the hot channel factors with respect to previous analysis for both high-enriched uranium (HEU) and low-enriched uranium (LEU) fuels. Although uncertainties in key parameters which enter into the analysis are not yet known for the LEU core, the current analysis uses reasonable approximations instead of conservative estimates based on HEU values. Cumulative distribution functions (CDFs) were obtained for critical heat flux ratio (CHFR), and onset of flow instability ratio (OFIR). As was done previously, the Sudo-Kaminaga correlation was used for CHF and the Saha-Zuber correlation was used for OFI. Results were obtained for probability levels of 90%, 95%, and 99.9%. As an example of the analysis, the results for both the existing reactor with HEU fuel and the LEU core show that CHFR would have to be above 1.39 to assure with 95% probability that there is no CHF. For the OFIR, the results show that the ratio should be above 1.40 to assure with a 95% probability that OFI is not reached.
Recent advances in statistical energy analysis
NASA Technical Reports Server (NTRS)
Heron, K. H.
1992-01-01
Statistical Energy Analysis (SEA) has traditionally been developed using modal summation and averaging approach, and has led to the need for many restrictive SEA assumptions. The assumption of 'weak coupling' is particularly unacceptable when attempts are made to apply SEA to structural coupling. It is now believed that this assumption is more a function of the modal formulation rather than a necessary formulation of SEA. The present analysis ignores this restriction and describes a wave approach to the calculation of plate-plate coupling loss factors. Predictions based on this method are compared with results obtained from experiments using point excitation on one side of an irregular six-sided box structure. Conclusions show that the use and calculation of infinite transmission coefficients is the way forward for the development of a purely predictive SEA code.
Statistical Analysis to Select Evacuation Route
NASA Astrophysics Data System (ADS)
Musyarof, Z.; Sutarto, D. Y.; Atika, D. R.; Fajriya Hakim, R. B.
2015-06-01
Each country should be responsible for the safety of people, especially responsible for the safety of people living in disaster-prone areas. One of those services is provides evacuation route for them. But all this time, the selection of evacuation route is seem does not well organized, it could be seen that when a disaster happen, there will be many accumulation of people on the steps of evacuation route. That condition is dangerous to people because hampers evacuation process. By some methods in Statistical analysis, author tries to give a suggestion how to prepare evacuation route which is organized and based on people habit. Those methods are association rules, sequential pattern mining, hierarchical cluster analysis and fuzzy logic.
HistFitter software framework for statistical data analysis
NASA Astrophysics Data System (ADS)
Baak, M.; Besjes, G. J.; Côté, D.; Koutsman, A.; Lorenz, J.; Short, D.
2015-04-01
We present a software framework for statistical data analysis, called HistFitter, that has been used extensively by the ATLAS Collaboration to analyze big datasets originating from proton-proton collisions at the Large Hadron Collider at CERN. Since 2012 HistFitter has been the standard statistical tool in searches for supersymmetric particles performed by ATLAS. HistFitter is a programmable and flexible framework to build, book-keep, fit, interpret and present results of data models of nearly arbitrary complexity. Starting from an object-oriented configuration, defined by users, the framework builds probability density functions that are automatically fit to data and interpreted with statistical tests. Internally HistFitter uses the statistics packages RooStats and HistFactory. A key innovation of HistFitter is its design, which is rooted in analysis strategies of particle physics. The concepts of control, signal and validation regions are woven into its fabric. These are progressively treated with statistically rigorous built-in methods. Being capable of working with multiple models at once that describe the data, HistFitter introduces an additional level of abstraction that allows for easy bookkeeping, manipulation and testing of large collections of signal hypotheses. Finally, HistFitter provides a collection of tools to present results with publication quality style through a simple command-line interface.
Environmental studies: Mathematical, computational, and statistical analysis
Wheeler, M.F.
1996-12-31
The Summer Program on Mathematical, Computational, and Statistical Analyses in Environmental Studies held 6--31 July 1992 was designed to provide a much needed interdisciplinary forum for joint exploration of recent advances in the formulation and application of (A) environmental models, (B) environmental data and data assimilation, (C) stochastic modeling and optimization, and (D) global climate modeling. These four conceptual frameworks provided common themes among a broad spectrum of specific technical topics at this workshop. The program brought forth a mix of physical concepts and processes such as chemical kinetics, atmospheric dynamics, cloud physics and dynamics, flow in porous media, remote sensing, climate statistical, stochastic processes, parameter identification, model performance evaluation, aerosol physics and chemistry, and data sampling together with mathematical concepts in stiff differential systems, advective-diffusive-reactive PDEs, inverse scattering theory, time series analysis, particle dynamics, stochastic equations, optimal control, and others. Nineteen papers are presented in this volume. Selected papers are indexed separately for inclusion in the Energy Science and Technology Database.
Multivariate statistical analysis of wildfires in Portugal
NASA Astrophysics Data System (ADS)
Costa, Ricardo; Caramelo, Liliana; Pereira, Mário
2013-04-01
Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).
On intracluster Faraday rotation. II - Statistical analysis
NASA Technical Reports Server (NTRS)
Lawler, J. M.; Dennison, B.
1982-01-01
The comparison of a reliable sample of radio source Faraday rotation measurements seen through rich clusters of galaxies, with sources seen through the outer parts of clusters and therefore having little intracluster Faraday rotation, indicates that the distribution of rotation in the former population is broadened, but only at the 80% level of statistical confidence. Employing a physical model for the intracluster medium in which the square root of magnetic field strength/turbulent cell per gas core radius number ratio equals approximately 0.07 microgauss, a Monte Carlo simulation is able to reproduce the observed broadening. An upper-limit analysis figure of less than 0.20 microgauss for the field strength/turbulent cell ratio, combined with lower limits on field strength imposed by limitations on the Compton-scattered flux, shows that intracluster magnetic fields must be tangled on scales greater than about 20 kpc.
Statistical analysis of ultrasonic measurements in concrete
NASA Astrophysics Data System (ADS)
Chiang, Chih-Hung; Chen, Po-Chih
2002-05-01
Stress wave techniques such as measurements of ultrasonic pulse velocity are often used to evaluate concrete quality in structures. For proper interpretation of measurement results, the dependence of pulse transit time on the average acoustic impedance and the material homogeneity along the sound path need to be examined. Semi-direct measurement of pulse velocity could be more convenient than through transmission measurement. It is not necessary to assess both sides of concrete floors or walls. A novel measurement scheme is proposed and verified based on statistical analysis. It is shown that Semi-direct measurements are very effective for gathering large amount of pulse velocity data from concrete reference specimens. The variability of measurements is comparable with that reported by American Concrete Institute using either break-off or pullout tests.
Additional EIPC Study Analysis. Final Report
Hadley, Stanton W; Gotham, Douglas J.; Luciani, Ralph L.
2014-12-01
Between 2010 and 2012 the Eastern Interconnection Planning Collaborative (EIPC) conducted a major long-term resource and transmission study of the Eastern Interconnection (EI). With guidance from a Stakeholder Steering Committee (SSC) that included representatives from the Eastern Interconnection States Planning Council (EISPC) among others, the project was conducted in two phases. Phase 1 involved a long-term capacity expansion analysis that involved creation of eight major futures plus 72 sensitivities. Three scenarios were selected for more extensive transmission- focused evaluation in Phase 2. Five power flow analyses, nine production cost model runs (including six sensitivities), and three capital cost estimations were developed during this second phase. The results from Phase 1 and 2 provided a wealth of data that could be examined further to address energy-related questions. A list of 14 topics was developed for further analysis. This paper brings together the earlier interim reports of the first 13 topics plus one additional topic into a single final report.
Analysis of Variance: What Is Your Statistical Software Actually Doing?
ERIC Educational Resources Information Center
Li, Jian; Lomax, Richard G.
2011-01-01
Users assume statistical software packages produce accurate results. In this article, the authors systematically examined Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) for 3 analysis of variance (ANOVA) designs, mixed-effects ANOVA, fixed-effects analysis of covariance (ANCOVA), and nested ANOVA. For each…
Statistical Analysis of Cardiovascular Data from FAP
NASA Technical Reports Server (NTRS)
Sealey, Meghan
2016-01-01
pressure, etc.) to see which could best predict how long the subjects could tolerate the tilt tests. With this I plan to analyze an artificial gravity study in order to determine the effects of orthostatic intolerance during spaceflight. From these projects, I became efficient in using the statistical software Stata, which I had previously never used before. I learned new statistical methods, such as mixed-effects linear regression, maximum likelihood estimation on longitudinal data, and post model-fitting tests to see if certain parameters contribute significantly to the model, all of which will better my understanding for when I continue studying for my masters' degree. I was also able to demonstrate my knowledge of statistics by helping other students run statistical analyses for their own projects. After completing these projects, the experience and knowledge gained from completing this analysis exemplifies the type of work that I would like to pursue in the future. After completing my masters' degree, I plan to pursue a career in biostatistics, which is exactly the position that I interned as, and I plan to use this experience to contribute to that goal
Statistical Analysis of Streamflow Trends in Slovenia
NASA Astrophysics Data System (ADS)
Jurko, M.; Kobold, M.; Mikoš, M.
2009-04-01
According to climate change, trends of river discharges were analyzed showing the hydrological change and future projections of hydrological behaviour in Slovenia. In last years droughts and floods are becoming more and more frequent. In the statistical analysis of streamflow trends of Slovenian rivers, available data on the low, mean and high discharges were examined using mean daily discharges and the Hydrospect software, which was developed under the auspices of WMO for detecting changes in hydrological data (Kundzewicz and Robson, 2000). The Mann-Kendall test was applied for the estimation of trends in the river flow index series. Trend analysis requires long records of observation to distinguish climate change-induced trends from climate variability. The problems of missing values, seasonal and other short-term fluctuations or anthropogenic impacts and lack of homogeneity of data due to the changes in instruments and observation techniques are frequently present in existing hydrological data sets. Therefore the analysis was carried out for 77 water gauging stations representatively distributed across Slovenia with sufficiently long and reliable continuous data sets. The average length of the data sets from the selected water gauging stations is about 50 years. Different indices were used to assess the temporal variation of discharges: annual mean daily discharge, annual maximum daily discharge, two magnitude and frequency series by peak-over-threshold (POT) approach (POT1 and POT3), and two low flow indices describing the different duration of low flows (7 and 30 days). The clustering method was used to classify the results of trends into groups. The assumption of a general decrease of water quantities in Slovenian rivers was confirmed. The annual mean daily discharges of the analyzed water gauging stations show a significant negative trend for the majority of the stations. Similar results with lower statistical significance show annual minimum 7-day and 30
An R package for statistical provenance analysis
NASA Astrophysics Data System (ADS)
Vermeesch, Pieter; Resentini, Alberto; Garzanti, Eduardo
2016-05-01
This paper introduces provenance, a software package within the statistical programming environment R, which aims to facilitate the visualisation and interpretation of large amounts of sedimentary provenance data, including mineralogical, petrographic, chemical and isotopic provenance proxies, or any combination of these. provenance comprises functions to: (a) calculate the sample size required to achieve a given detection limit; (b) plot distributional data such as detrital zircon U-Pb age spectra as Cumulative Age Distributions (CADs) or adaptive Kernel Density Estimates (KDEs); (c) plot compositional data as pie charts or ternary diagrams; (d) correct the effects of hydraulic sorting on sandstone petrography and heavy mineral composition; (e) assess the settling equivalence of detrital minerals and grain-size dependence of sediment composition; (f) quantify the dissimilarity between distributional data using the Kolmogorov-Smirnov and Sircombe-Hazelton distances, or between compositional data using the Aitchison and Bray-Curtis distances; (e) interpret multi-sample datasets by means of (classical and nonmetric) Multidimensional Scaling (MDS) and Principal Component Analysis (PCA); and (f) simplify the interpretation of multi-method datasets by means of Generalised Procrustes Analysis (GPA) and 3-way MDS. All these tools can be accessed through an intuitive query-based user interface, which does not require knowledge of the R programming language. provenance is free software released under the GPL-2 licence and will be further expanded based on user feedback.
Time Series Analysis Based on Running Mann Whitney Z Statistics
Technology Transfer Automated Retrieval System (TEKTRAN)
A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...
Statistical analysis of high density diffuse optical tomography
Hassanpour, Mahlega S.; White, Brian R.; Eggebrecht, Adam T.; Ferradal, Silvina L.; Snyder, Abraham Z.; Culver, Joseph P.
2014-01-01
High density diffuse optical tomography (HD-DOT) is a noninvasive neuroimaging modality with moderate spatial resolution and localization accuracy. Due to portability and wear-ability advantages, HD-DOT has the potential to be used in populations that are not amenable to functional magnetic resonance imaging (fMRI), such as hospitalized patients and young children. However, whereas the use of event-related stimuli designs, general linear model (GLM) analysis, and imaging statistics are standardized and routine with fMRI, such tools are not yet common practice in HD-DOT. In this paper we adapt and optimize fundamental elements of fMRI analysis for application to HD-DOT. We show the use of event-related protocols and GLM de-convolution analysis in un-mixing multi-stimuli event-related HD-DOT data. Statistical parametric mapping (SPM) in the framework of a general linear model is developed considering the temporal and spatial characteristics of HD- DOT data. The statistical analysis utilizes a random field noise model that incorporates estimates of the local temporal and spatial correlations of the GLM residuals. The multiple-comparison problem is addressed using a cluster analysis based on non-stationary Gaussian random field theory. These analysis tools provide access to a wide range of experimental designs necessary for the study of the complex brain functions. In addition, they provide a foundation for understanding and interpreting HD-DOT results with quantitative estimates for the statistical significance of detected activation foci. PMID:23732886
Statistical Analysis of Single-Trial Granger Causality Spectra
Brovelli, Andrea
2012-01-01
Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based on t-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity. PMID:22649482
CORSSA: Community Online Resource for Statistical Seismicity Analysis
NASA Astrophysics Data System (ADS)
Zechar, J. D.; Hardebeck, J. L.; Michael, A. J.; Naylor, M.; Steacy, S.; Wiemer, S.; Zhuang, J.
2011-12-01
Statistical seismology is critical to the understanding of seismicity, the evaluation of proposed earthquake prediction and forecasting methods, and the assessment of seismic hazard. Unfortunately, despite its importance to seismology-especially to those aspects with great impact on public policy-statistical seismology is mostly ignored in the education of seismologists, and there is no central repository for the existing open-source software tools. To remedy these deficiencies, and with the broader goal to enhance the quality of statistical seismology research, we have begun building the Community Online Resource for Statistical Seismicity Analysis (CORSSA, www.corssa.org). We anticipate that the users of CORSSA will range from beginning graduate students to experienced researchers. More than 20 scientists from around the world met for a week in Zurich in May 2010 to kick-start the creation of CORSSA: the format and initial table of contents were defined; a governing structure was organized; and workshop participants began drafting articles. CORSSA materials are organized with respect to six themes, each will contain between four and eight articles. CORSSA now includes seven articles with an additional six in draft form along with forums for discussion, a glossary, and news about upcoming meetings, special issues, and recent papers. Each article is peer-reviewed and presents a balanced discussion, including illustrative examples and code snippets. Topics in the initial set of articles include: introductions to both CORSSA and statistical seismology, basic statistical tests and their role in seismology; understanding seismicity catalogs and their problems; basic techniques for modeling seismicity; and methods for testing earthquake predictability hypotheses. We have also begun curating a collection of statistical seismology software packages.
Building the Community Online Resource for Statistical Seismicity Analysis (CORSSA)
NASA Astrophysics Data System (ADS)
Michael, A. J.; Wiemer, S.; Zechar, J. D.; Hardebeck, J. L.; Naylor, M.; Zhuang, J.; Steacy, S.; Corssa Executive Committee
2010-12-01
Statistical seismology is critical to the understanding of seismicity, the testing of proposed earthquake prediction and forecasting methods, and the assessment of seismic hazard. Unfortunately, despite its importance to seismology - especially to those aspects with great impact on public policy - statistical seismology is mostly ignored in the education of seismologists, and there is no central repository for the existing open-source software tools. To remedy these deficiencies, and with the broader goal to enhance the quality of statistical seismology research, we have begun building the Community Online Resource for Statistical Seismicity Analysis (CORSSA). CORSSA is a web-based educational platform that is authoritative, up-to-date, prominent, and user-friendly. We anticipate that the users of CORSSA will range from beginning graduate students to experienced researchers. More than 20 scientists from around the world met for a week in Zurich in May 2010 to kick-start the creation of CORSSA: the format and initial table of contents were defined; a governing structure was organized; and workshop participants began drafting articles. CORSSA materials are organized with respect to six themes, each containing between four and eight articles. The CORSSA web page, www.corssa.org, officially unveiled on September 6, 2010, debuts with an initial set of approximately 10 to 15 articles available online for viewing and commenting with additional articles to be added over the coming months. Each article will be peer-reviewed and will present a balanced discussion, including illustrative examples and code snippets. Topics in the initial set of articles will include: introductions to both CORSSA and statistical seismology, basic statistical tests and their role in seismology; understanding seismicity catalogs and their problems; basic techniques for modeling seismicity; and methods for testing earthquake predictability hypotheses. A special article will compare and review
SPA- STATISTICAL PACKAGE FOR TIME AND FREQUENCY DOMAIN ANALYSIS
NASA Technical Reports Server (NTRS)
Brownlow, J. D.
1994-01-01
The need for statistical analysis often arises when data is in the form of a time series. This type of data is usually a collection of numerical observations made at specified time intervals. Two kinds of analysis may be performed on the data. First, the time series may be treated as a set of independent observations using a time domain analysis to derive the usual statistical properties including the mean, variance, and distribution form. Secondly, the order and time intervals of the observations may be used in a frequency domain analysis to examine the time series for periodicities. In almost all practical applications, the collected data is actually a mixture of the desired signal and a noise signal which is collected over a finite time period with a finite precision. Therefore, any statistical calculations and analyses are actually estimates. The Spectrum Analysis (SPA) program was developed to perform a wide range of statistical estimation functions. SPA can provide the data analyst with a rigorous tool for performing time and frequency domain studies. In a time domain statistical analysis the SPA program will compute the mean variance, standard deviation, mean square, and root mean square. It also lists the data maximum, data minimum, and the number of observations included in the sample. In addition, a histogram of the time domain data is generated, a normal curve is fit to the histogram, and a goodness-of-fit test is performed. These time domain calculations may be performed on both raw and filtered data. For a frequency domain statistical analysis the SPA program computes the power spectrum, cross spectrum, coherence, phase angle, amplitude ratio, and transfer function. The estimates of the frequency domain parameters may be smoothed with the use of Hann-Tukey, Hamming, Barlett, or moving average windows. Various digital filters are available to isolate data frequency components. Frequency components with periods longer than the data collection interval
Statistical Analysis of Nondisjunction Assays in Drosophila
Zeng, Yong; Li, Hua; Schweppe, Nicole M.; Hawley, R. Scott; Gilliland, William D.
2010-01-01
Many advances in the understanding of meiosis have been made by measuring how often errors in chromosome segregation occur. This process of nondisjunction can be studied by counting experimental progeny, but direct measurement of nondisjunction rates is complicated by not all classes of nondisjunctional progeny being viable. For X chromosome nondisjunction in Drosophila female meiosis, all of the normal progeny survive, while nondisjunctional eggs produce viable progeny only if fertilized by sperm that carry the appropriate sex chromosome. The rate of nondisjunction has traditionally been estimated by assuming a binomial process and doubling the number of observed nondisjunctional progeny, to account for the inviable classes. However, the correct way to derive statistics (such as confidence intervals or hypothesis testing) by this approach is far from clear. Instead, we use the multinomial-Poisson hierarchy model and demonstrate that the old estimator is in fact the maximum-likelihood estimator (MLE). Under more general assumptions, we derive asymptotic normality of this estimator and construct confidence interval and hypothesis testing formulae. Confidence intervals under this framework are always larger than under the binomial framework, and application to published data shows that use of the multinomial approach can avoid an apparent type 1 error made by use of the binomial assumption. The current study provides guidance for researchers designing genetic experiments on nondisjunction and improves several methods for the analysis of genetic data. PMID:20660647
Statistical approach to partial equilibrium analysis
NASA Astrophysics Data System (ADS)
Wang, Yougui; Stanley, H. E.
2009-04-01
A statistical approach to market equilibrium and efficiency analysis is proposed in this paper. One factor that governs the exchange decisions of traders in a market, named willingness price, is highlighted and constitutes the whole theory. The supply and demand functions are formulated as the distributions of corresponding willing exchange over the willingness price. The laws of supply and demand can be derived directly from these distributions. The characteristics of excess demand function are analyzed and the necessary conditions for the existence and uniqueness of equilibrium point of the market are specified. The rationing rates of buyers and sellers are introduced to describe the ratio of realized exchange to willing exchange, and their dependence on the market price is studied in the cases of shortage and surplus. The realized market surplus, which is the criterion of market efficiency, can be written as a function of the distributions of willing exchange and the rationing rates. With this approach we can strictly prove that a market is efficient in the state of equilibrium.
Statistical energy analysis of nonlinear vibrating systems.
Spelman, G M; Langley, R S
2015-09-28
Nonlinearities in practical systems can arise in contacts between components, possibly from friction or impacts. However, it is also known that quadratic and cubic nonlinearity can occur in the stiffness of structural elements undergoing large amplitude vibration, without the need for local contacts. Nonlinearity due purely to large amplitude vibration can then result in significant energy being found in frequency bands other than those being driven by external forces. To analyse this phenomenon, a method is developed here in which the response of the structure in the frequency domain is divided into frequency bands, and the energy flow between the frequency bands is calculated. The frequency bands are assigned an energy variable to describe the mean response and the nonlinear coupling between bands is described in terms of weighted summations of the convolutions of linear modal transfer functions. This represents a nonlinear extension to an established linear theory known as statistical energy analysis (SEA). The nonlinear extension to SEA theory is presented for the case of a plate structure with quadratic and cubic nonlinearity. PMID:26303923
Web-Based Statistical Sampling and Analysis
ERIC Educational Resources Information Center
Quinn, Anne; Larson, Karen
2016-01-01
Consistent with the Common Core State Standards for Mathematics (CCSSI 2010), the authors write that they have asked students to do statistics projects with real data. To obtain real data, their students use the free Web-based app, Census at School, created by the American Statistical Association (ASA) to help promote civic awareness among school…
Statistical Error Analysis for Digital Recursive Filters
NASA Astrophysics Data System (ADS)
Wu, Kevin Chi-Rung
The study of arithmetic roundoff error has attracted many researchers to investigate how the signal-to-noise ratio (SNR) is affected by algorithmic parameters, especially since the VLSI (Very Large Scale Integrated circuits) technologies have become more promising for digital signal processing. Typically, digital signal processing involving, either with or without matrix inversion, will have tradeoffs on speed and processor cost. Hence, the problems of an area-time efficient matrix computation and roundoff error behavior analysis will play an important role in this dissertation. A newly developed non-Cholesky square-root matrix will be discussed which precludes the arithmetic roundoff error over some interesting operations, such as complex -valued matrix inversion with its SNR analysis and error propagation effects. A non-CORDIC parallelism approach for complex-valued matrix will be presented to upgrade speed at the cost of moderate increase of processor. The lattice filter will also be looked into, in such a way, that one can understand the SNR behavior under the conditions of different inputs in the joint process system. Pipelining technique will be demonstrated to manifest the possibility of high-speed non-matrix-inversion lattice filter. Floating point arithmetic modelings used in this study have been focused on effective methodologies that have been proved to be reliable and feasible. With the models in hand, we study the roundoff error behavior based on some statistical assumptions. Results are demonstrated by carrying out simulation to show the feasibility of SNR analysis. We will observe that non-Cholesky square-root matrix has advantage of saving a time of O(n^3) as well as a reduced realization cost. It will be apparent that for a Kalman filter the register size is increasing significantly, if pole of the system matrix is moving closer to the edge of the unit circle. By comparing roundoff error effect due to floating-point and fixed-point arithmetics, we
Statistical Analysis of Examination to Detect Cheating.
ERIC Educational Resources Information Center
Code, Ronald P.
1985-01-01
A number of statistical procedures that were developed in 1983 at the University of Medicine and Dentistry of New Jersey-Rutgers Medical School to verify the suspicion that a student cheated during an examination are described. (MLW)
{chi}{sup 2} versus median statistics in supernova type Ia data analysis
Barreira, A.; Avelino, P. P.
2011-10-15
In this paper we compare the performances of the {chi}{sup 2} and median likelihood analysis in the determination of cosmological constraints using type Ia supernovae data. We perform a statistical analysis using the 307 supernovae of the Union 2 compilation of the Supernova Cosmology Project and find that the {chi}{sup 2} statistical analysis yields tighter cosmological constraints than the median statistic if only supernovae data is taken into account. We also show that when additional measurements from the cosmic microwave background and baryonic acoustic oscillations are considered, the combined cosmological constraints are not strongly dependent on whether one applies the {chi}{sup 2} statistic or the median statistic to the supernovae data. This indicates that, when complementary information from other cosmological probes is taken into account, the performances of the {chi}{sup 2} and median statistics are very similar, demonstrating the robustness of the statistical analysis.
Acid Rain Analysis by Standard Addition Titration.
ERIC Educational Resources Information Center
Ophardt, Charles E.
1985-01-01
The standard addition titration is a precise and rapid method for the determination of the acidity in rain or snow samples. The method requires use of a standard buret, a pH meter, and Gran's plot to determine the equivalence point. Experimental procedures used and typical results obtained are presented. (JN)
Notes on numerical reliability of several statistical analysis programs
Landwehr, J.M.; Tasker, Gary D.
1999-01-01
This report presents a benchmark analysis of several statistical analysis programs currently in use in the USGS. The benchmark consists of a comparison between the values provided by a statistical analysis program for variables in the reference data set ANASTY and their known or calculated theoretical values. The ANASTY data set is an amendment of the Wilkinson NASTY data set that has been used in the statistical literature to assess the reliability (computational correctness) of calculated analytical results.
Statistical Analysis of Refractivity in UAE
NASA Astrophysics Data System (ADS)
Al-Ansari, Kifah; Al-Mal, Abdulhadi Abu; Kamel, Rami
2007-07-01
This paper presents the results of the refractivity statistics in the UAE (United Arab Emirates) for a period of 14 years (1990-2003). Six sites have been considered using meteorological surface data (Abu Dhabi, Dubai, Sharjah, Al-Ain, Ras Al-Kaimah, and Al-Fujairah). Upper air (radiosonde) data were available at one site only, Abu Dhabi airport, which has been considered for the refractivity gradient statistics. Monthly and yearly averages are obtained for the two parameters, refractivity and refractivity gradient. Cumulative distributions are also provided.
Statistical measures for workload capacity analysis.
Houpt, Joseph W; Townsend, James T
2012-10-01
A critical component of how we understand a mental process is given by measuring the effect of varying the workload. The capacity coefficient (Townsend & Nozawa, 1995; Townsend & Wenger, 2004) is a measure on response times for quantifying changes in performance due to workload. Despite its precise mathematical foundation, until now rigorous statistical tests have been lacking. In this paper, we demonstrate statistical properties of the components of the capacity measure and propose a significance test for comparing the capacity coefficient to a baseline measure or two capacity coefficients to each other. PMID:23175582
EXPERIMENTAL DESIGN: STATISTICAL CONSIDERATIONS AND ANALYSIS
Technology Transfer Automated Retrieval System (TEKTRAN)
In this book chapter, information on how field experiments in invertebrate pathology are designed and the data collected, analyzed, and interpreted is presented. The practical and statistical issues that need to be considered and the rationale and assumptions behind different designs or procedures ...
Statistical analysis of pitting corrosion in condenser tubes
Ault, J.P.; Gehring, G.A. Jr.
1997-12-31
Condenser tube failure via wall penetration allows cooling water to contaminate the working fluid (steam). Contamination, especially from brackish or saltwater, will lower steam quality and thus lower overall plant efficiency. Because of the importance of minimizing leakages, power plant engineers are primarily concerned with the maximum localized corrosion in a unit rather than average corrosion values or rates. Extreme value analysis is a useful tool for evaluating the condition of condenser tubing. Extreme value statistical techniques allow the prediction of the most probable deepest pit in a given surface area based upon data acquired from a smaller surface area. Data is gathered from a physical examination of actual tubes (either in-service or from a sidestream unit) rather than small sample coupons. Three distinct applications of extreme value statistics to condenser tube evaluation are presented in this paper: (1) condition assessment of an operating condenser, (2) design data for material selection, and (3) research tool for assessing impact of various factors on condenser tube corrosion. The projections for operating units based on extreme value analysis are shown to be more useful than those made on the basis of other techniques such as eddy current or electrochemical measurements. Extreme value analysis would benefit from advances in two key areas: (1) development of an accurate and economical method for the measurement of maximum pit depths of condenser tubes in-situ would enhance the application of extreme value statistical analysis to the assessment of condenser tubing corrosion pitting and (2) development of methodologies to predict pit depth-time relationship in addition to pit depth-area relationship would be useful for modeling purposes.
a Multivariate Statistical Analysis of Visibility at California Regions.
NASA Astrophysics Data System (ADS)
Motallebi, Nehzat
This study summarizes the results of a comprehensive study of visibility in California. California is one of the few states that has promulgated air quality standards for visibility. The study was concerned not only with major metropolitan areas such as Los Angeles, but also with deterioration of visibility in the less urbanized areas of California. The relationships among visibility reduction, atmospheric pollutants, and meteorological conditions were examined by using the multivariate statistical techniques of principal component analysis and multiple linear regression analysis. The primary concern of this work was to find which of the many atmospheric constituents most effectively reduce visibility, and to determine the role of the different meteorological variables on these relationships. Another objective was to identify the major pollutant sources and transport routes which contribute to visibility degradation. In order to establish the relationship between the light scattering coefficient and particulate data, both the size distribution and the elemental composition of particulate aerosols were considered. Meanwhile, including meteorological parameters in the principal component analysis made it possible to investigate meteorological effects on the observed pollution patterns. The associations among wind direction, elemental concentration, and additional meteorological parameters were considered by using a special modification of principal component analysis. This technique can identify all of the main features, and provides reasonable source direction for particular elements. It is appropriate to note that there appeared to be no published accounts of a principal component analysis for a data set similar to that analyzed in this work. Finally, the results of the multivariate statistical analyses, multiple linear regression analysis and principal component analysis, indicate that intermediate size sulfur containing aerosols, sulfur size mode 0.6 (mu)m < D
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.
A statistical package for computing time and frequency domain analysis
NASA Technical Reports Server (NTRS)
Brownlow, J.
1978-01-01
The spectrum analysis (SPA) program is a general purpose digital computer program designed to aid in data analysis. The program does time and frequency domain statistical analyses as well as some preanalysis data preparation. The capabilities of the SPA program include linear trend removal and/or digital filtering of data, plotting and/or listing of both filtered and unfiltered data, time domain statistical characterization of data, and frequency domain statistical characterization of data.
Computer program performs statistical analysis for random processes
NASA Technical Reports Server (NTRS)
Newberry, M. H.
1966-01-01
Random Vibration Analysis Program /RAVAN/ performs statistical analysis on a number of phenomena associated with flight and captive tests, but can also be used in analyzing data from many other random processes.
Statistical Analysis of Random Number Generators
NASA Astrophysics Data System (ADS)
Accardi, Luigi; Gäbler, Markus
2011-01-01
In many applications, for example cryptography and Monte Carlo simulation, there is need for random numbers. Any procedure, algorithm or device which is intended to produce such is called a random number generator (RNG). What makes a good RNG? This paper gives an overview on empirical testing of the statistical properties of the sequences produced by RNGs and special software packages designed for that purpose. We also present the results of applying a particular test suite--TestU01-- to a family of RNGs currently being developed at the Centro Interdipartimentale Vito Volterra (CIVV), Roma, Italy.
On the statistical analysis of maximal magnitude
NASA Astrophysics Data System (ADS)
Holschneider, M.; Zöller, G.; Hainzl, S.
2012-04-01
We show how the maximum expected magnitude within a time horizon [0,T] may be estimated from earthquake catalog data within the context of truncated Gutenberg-Richter statistics. We present the results in a frequentist and in a Bayesian setting. Instead of deriving point estimations of this parameter and reporting its performance in terms of expectation value and variance, we focus on the calculation of confidence intervals based on an imposed level of confidence α. We present an estimate of the maximum magnitude within an observational time interval T in the future, given a complete earthquake catalog for a time period Tc in the past and optionally some paleoseismic events. We argue that from a statistical point of view the maximum magnitude in a time window is a reasonable parameter for probabilistic seismic hazard assessment, while the commonly used maximum possible magnitude for all times does almost certainly not allow the calculation of useful (i.e. non-trivial) confidence intervals. In the context of an unbounded GR law we show, that Jeffreys invariant prior distribtution yields normalizable posteriors. The predictive distribution based on this prior is explicitely computed.
Statistical analysis of life history calendar data.
Eerola, Mervi; Helske, Satu
2016-04-01
The life history calendar is a data-collection tool for obtaining reliable retrospective data about life events. To illustrate the analysis of such data, we compare the model-based probabilistic event history analysis and the model-free data mining method, sequence analysis. In event history analysis, we estimate instead of transition hazards the cumulative prediction probabilities of life events in the entire trajectory. In sequence analysis, we compare several dissimilarity metrics and contrast data-driven and user-defined substitution costs. As an example, we study young adults' transition to adulthood as a sequence of events in three life domains. The events define the multistate event history model and the parallel life domains in multidimensional sequence analysis. The relationship between life trajectories and excess depressive symptoms in middle age is further studied by their joint prediction in the multistate model and by regressing the symptom scores on individual-specific cluster indices. The two approaches complement each other in life course analysis; sequence analysis can effectively find typical and atypical life patterns while event history analysis is needed for causal inquiries. PMID:23117406
Statistical Seismic Landslide Analysis: an Update
NASA Astrophysics Data System (ADS)
Lee, Chyi-Tyi
2015-04-01
Landslides are secondary or induced features, whose recurrence is controlled by the repetition of triggering events, such as earthquakes or heavy rainfall. This makes seismic landslide hazard analysis more complicated than ordinary seismic hazard analysis, and it requires multi-stage analysis. First, susceptibility analysis is utilized to divide a region into successive classes. Then, it is necessary to construct a relationship between the probability of landslide failure and earthquake intensity for each susceptibility class for a region, or to find the probability of failure surface using the susceptibility value and earthquake intensity as independent variables at the study region. Then, hazard analysis for the exceedance probability of earthquake intensity is performed. Finally, an analysis of the spatial probability of landslide failure under a certain return-period earthquake is drawn. This study uses data for Chi-Chi earthquake induced landslides as the training data set to perform the susceptibility analysis and probability of failure surface analysis. A regular probabilistic seismic hazard analysis is also conducted to map different return-period Arias intensities. Finally a seismic landslide hazard map for the whole of Taiwan is provided.
Tanavalee, Chotetawan; Luksanapruksa, Panya; Singhatanadgige, Weerasak
2016-06-01
Microsoft Excel (MS Excel) is a commonly used program for data collection and statistical analysis in biomedical research. However, this program has many limitations, including fewer functions that can be used for analysis and a limited number of total cells compared with dedicated statistical programs. MS Excel cannot complete analyses with blank cells, and cells must be selected manually for analysis. In addition, it requires multiple steps of data transformation and formulas to plot survival analysis graphs, among others. The Megastat add-on program, which will be supported by MS Excel 2016 soon, would eliminate some limitations of using statistic formulas within MS Excel. PMID:27135620
Diagnostic rhyme test statistical analysis programs
NASA Astrophysics Data System (ADS)
Sim, A.; Bain, R.; Belyavin, A. J.; Pratt, R. L.
1991-08-01
The statistical techniques and associated computer programs used to analyze data from Diagnostic Rhyme Test (DRT) are described. The DRT is used extensively for assessing the intelligibility of military communications systems and became an accepted NATO standard for testing linear predictive coders. The DRT vocabulary comprises ninety six minimally contrasting rhyming word pairs, the initial consonants of which differ only by a single acoustic feature, or attribute. There are six such attributes: voicing, nasality, sustention, silibation, graveness, and compactness. The attribute voicing is present when the vocal cords are excited: in the word pair 'veal-feel', the consonant 'v' is voiced, but the constant 'f' is unvoiced. The procedure for the implementation of the DRT is presented. To ensure the stability of the results, tests using not less than eight talkers and eight listeners are conducted.
Statistics over features: EEG signals analysis.
Derya Ubeyli, Elif
2009-08-01
This paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes. PMID:19555931
Statistical analysis of low level atmospheric turbulence
NASA Technical Reports Server (NTRS)
Tieleman, H. W.; Chen, W. W. L.
1974-01-01
The statistical properties of low-level wind-turbulence data were obtained with the model 1080 total vector anemometer and the model 1296 dual split-film anemometer, both manufactured by Thermo Systems Incorporated. The data obtained from the above fast-response probes were compared with the results obtained from a pair of Gill propeller anemometers. The digitized time series representing the three velocity components and the temperature were each divided into a number of blocks, the length of which depended on the lowest frequency of interest and also on the storage capacity of the available computer. A moving-average and differencing high-pass filter was used to remove the trend and the low frequency components in the time series. The calculated results for each of the anemometers used are represented in graphical or tabulated form.
Statistical Analysis of Galaxy Redshift Surveys
NASA Astrophysics Data System (ADS)
Percival, Will J.
2008-12-01
The statistical distribution of galaxies encodes significant cosmological information. For Gaussian random fields, 2-point functions, the correlation function in real space and the power spectrum in Fourier space are complete, and offer the most direct route to this information. In this proceedings, I consider three mechanisms for extracting information from the power spectrum. The relative amplitude of small-scale and large-scale power can constrain the matter-radiation equality scale, but this is hard to disentangle from galaxy bias. Baryon Acoustic Oscillations are more robust to galaxy bias effects, and lead to constraints the evolution of the Universe by providing a standard ruler whose distance can be compared at different redshifts. Redshift-Space distortions, resulting from galaxy peculiar velocities can be used to measure the cosmological growth of structure, and are immune to density bias as the velocities are independent of galaxy properties.
Comparative analysis of positive and negative attitudes toward statistics
NASA Astrophysics Data System (ADS)
Ghulami, Hassan Rahnaward; Ab Hamid, Mohd Rashid; Zakaria, Roslinazairimah
2015-02-01
Many statistics lecturers and statistics education researchers are interested to know the perception of their students' attitudes toward statistics during the statistics course. In statistics course, positive attitude toward statistics is a vital because it will be encourage students to get interested in the statistics course and in order to master the core content of the subject matters under study. Although, students who have negative attitudes toward statistics they will feel depressed especially in the given group assignment, at risk for failure, are often highly emotional, and could not move forward. Therefore, this study investigates the students' attitude towards learning statistics. Six latent constructs have been the measurement of students' attitudes toward learning statistic such as affect, cognitive competence, value, difficulty, interest, and effort. The questionnaire was adopted and adapted from the reliable and validate instrument of Survey of Attitudes towards Statistics (SATS). This study is conducted among engineering undergraduate engineering students in the university Malaysia Pahang (UMP). The respondents consist of students who were taking the applied statistics course from different faculties. From the analysis, it is found that the questionnaire is acceptable and the relationships among the constructs has been proposed and investigated. In this case, students show full effort to master the statistics course, feel statistics course enjoyable, have confidence that they have intellectual capacity, and they have more positive attitudes then negative attitudes towards statistics learning. In conclusion in terms of affect, cognitive competence, value, interest and effort construct the positive attitude towards statistics was mostly exhibited. While negative attitudes mostly exhibited by difficulty construct.
Statistical Evaluation of Time Series Analysis Techniques
NASA Technical Reports Server (NTRS)
Benignus, V. A.
1973-01-01
The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.
CORSSA: The Community Online Resource for Statistical Seismicity Analysis
Michael, Andrew J.; Wiemer, Stefan
2010-01-01
Statistical seismology is the application of rigorous statistical methods to earthquake science with the goal of improving our knowledge of how the earth works. Within statistical seismology there is a strong emphasis on the analysis of seismicity data in order to improve our scientific understanding of earthquakes and to improve the evaluation and testing of earthquake forecasts, earthquake early warning, and seismic hazards assessments. Given the societal importance of these applications, statistical seismology must be done well. Unfortunately, a lack of educational resources and available software tools make it difficult for students and new practitioners to learn about this discipline. The goal of the Community Online Resource for Statistical Seismicity Analysis (CORSSA) is to promote excellence in statistical seismology by providing the knowledge and resources necessary to understand and implement the best practices, so that the reader can apply these methods to their own research. This introduction describes the motivation for and vision of CORRSA. It also describes its structure and contents.
Improved Statistics for Genome-Wide Interaction Analysis
Ueki, Masao; Cordell, Heather J.
2012-01-01
Recently, Wu and colleagues [1] proposed two novel statistics for genome-wide interaction analysis using case/control or case-only data. In computer simulations, their proposed case/control statistic outperformed competing approaches, including the fast-epistasis option in PLINK and logistic regression analysis under the correct model; however, reasons for its superior performance were not fully explored. Here we investigate the theoretical properties and performance of Wu et al.'s proposed statistics and explain why, in some circumstances, they outperform competing approaches. Unfortunately, we find minor errors in the formulae for their statistics, resulting in tests that have higher than nominal type 1 error. We also find minor errors in PLINK's fast-epistasis and case-only statistics, although theory and simulations suggest that these errors have only negligible effect on type 1 error. We propose adjusted versions of all four statistics that, both theoretically and in computer simulations, maintain correct type 1 error rates under the null hypothesis. We also investigate statistics based on correlation coefficients that maintain similar control of type 1 error. Although designed to test specifically for interaction, we show that some of these previously-proposed statistics can, in fact, be sensitive to main effects at one or both loci, particularly in the presence of linkage disequilibrium. We propose two new “joint effects” statistics that, provided the disease is rare, are sensitive only to genuine interaction effects. In computer simulations we find, in most situations considered, that highest power is achieved by analysis under the correct genetic model. Such an analysis is unachievable in practice, as we do not know this model. However, generally high power over a wide range of scenarios is exhibited by our joint effects and adjusted Wu statistics. We recommend use of these alternative or adjusted statistics and urge caution when using Wu et al
Importance of data management with statistical analysis set division.
Wang, Ling; Li, Chan-juan; Jiang, Zhi-wei; Xia, Jie-lai
2015-11-01
Testing of hypothesis was affected by statistical analysis set division which was an important data management work before data base lock-in. Objective division of statistical analysis set under blinding was the guarantee of scientific trial conclusion. All the subjects having accepted at least once trial treatment after randomization should be concluded in safety set. Full analysis set should be close to the intention-to-treat as far as possible. Per protocol set division was the most difficult to control in blinded examination because of more subjectivity than the other two. The objectivity of statistical analysis set division must be guaranteed by the accurate raw data, the comprehensive data check and the scientific discussion, all of which were the strict requirement of data management. Proper division of statistical analysis set objectively and scientifically is an important approach to improve the data management quality. PMID:26911044
Statistical analysis of the 'Almagest' star catalog
NASA Astrophysics Data System (ADS)
Kalashnikov, V. V.; Nosovskii, G. V.; Fomenko, A. T.
The star catalog contained in the 'Almagest', Ptolemy's classical work of astronomy, is examined. An analysis method is proposed which allows the identification of various types of errors committed by the observer. This method not only removes many of the contradictions contained in the catalog but also makes it possible to determine the time period during which the catalog was compiled.
Statistical analysis of fixed income market
NASA Astrophysics Data System (ADS)
Bernaschi, Massimo; Grilli, Luca; Vergni, Davide
2002-05-01
We present cross and time series analysis of price fluctuations in the US Treasury fixed income market. Bonds have been classified according to a suitable metric based on the correlation among them. The classification shows how the correlation among fixed income securities depends strongly on their maturity. We study also the structure of price fluctuations for single time series.
A Realistic Experimental Design and Statistical Analysis Project
ERIC Educational Resources Information Center
Muske, Kenneth R.; Myers, John A.
2007-01-01
A realistic applied chemical engineering experimental design and statistical analysis project is documented in this article. This project has been implemented as part of the professional development and applied statistics courses at Villanova University over the past five years. The novel aspects of this project are that the students are given a…
Internet Data Analysis for the Undergraduate Statistics Curriculum
ERIC Educational Resources Information Center
Sanchez, Juana; He, Yan
2005-01-01
Statistics textbooks for undergraduates have not caught up with the enormous amount of analysis of Internet data that is taking place these days. Case studies that use Web server log data or Internet network traffic data are rare in undergraduate Statistics education. And yet these data provide numerous examples of skewed and bimodal…
Guidelines for Statistical Analysis of Percentage of Syllables Stuttered Data
ERIC Educational Resources Information Center
Jones, Mark; Onslow, Mark; Packman, Ann; Gebski, Val
2006-01-01
Purpose: The purpose of this study was to develop guidelines for the statistical analysis of percentage of syllables stuttered (%SS) data in stuttering research. Method; Data on %SS from various independent sources were used to develop a statistical model to describe this type of data. On the basis of this model, %SS data were simulated with…
Explorations in Statistics: The Analysis of Ratios and Normalized Data
ERIC Educational Resources Information Center
Curran-Everett, Douglas
2013-01-01
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This ninth installment of "Explorations in Statistics" explores the analysis of ratios and normalized--or standardized--data. As researchers, we compute a ratio--a numerator divided by a denominator--to compute a…
Statistical image analysis of longitudinal RAVENS images
Lee, Seonjoo; Zipunnikov, Vadim; Reich, Daniel S.; Pham, Dzung L.
2015-01-01
Regional analysis of volumes examined in normalized space (RAVENS) are transformation images used in the study of brain morphometry. In this paper, RAVENS images are analyzed using a longitudinal variant of voxel-based morphometry (VBM) and longitudinal functional principal component analysis (LFPCA) for high-dimensional images. We demonstrate that the latter overcomes the limitations of standard longitudinal VBM analyses, which does not separate registration errors from other longitudinal changes and baseline patterns. This is especially important in contexts where longitudinal changes are only a small fraction of the overall observed variability, which is typical in normal aging and many chronic diseases. Our simulation study shows that LFPCA effectively separates registration error from baseline and longitudinal signals of interest by decomposing RAVENS images measured at multiple visits into three components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the irreversible changes over multiple visits, and a subject-visit specific imaging deviation. We describe strategies to identify baseline/longitudinal variation and registration errors combined with covariates of interest. Our analysis suggests that specific regional brain atrophy and ventricular enlargement are associated with multiple sclerosis (MS) disease progression. PMID:26539071
Statistical models and NMR analysis of polymer microstructure
Technology Transfer Automated Retrieval System (TEKTRAN)
Statistical models can be used in conjunction with NMR spectroscopy to study polymer microstructure and polymerization mechanisms. Thus, Bernoullian, Markovian, and enantiomorphic-site models are well known. Many additional models have been formulated over the years for additional situations. Typica...
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.
Improving the Statistical Methodology of Astronomical Data Analysis
NASA Astrophysics Data System (ADS)
Feigelson, Eric D.; Babu, Gutti Jogesh
Contemporary observational astronomers are generally unfamiliar with the extensive advances made in mathematical and applied statistics during the past several decades. Astronomical problems can often be addressed by methods developed in statistical fields such as spatial point processes, density estimation, Bayesian statistics, and sampling theory. The common problem of bivariate linear regression illustrates the need for sophisticated methods. Astronomical problems often require combinations of ordinary least-squares lines, double-weighted and errors-in-variables models, censored and truncated regressions, each with its own error analysis procedure. The recent conference Statistical Challenges in Modern Astronomy highlighted issues of mutual interest to statisticians and astronomers including clustering of point processes and time series analysis. We conclude with advice on how the astronomical community can advance its statistical methodology with improvements in education of astrophysicists, collaboration and consultation with professional statisticians, and acquisition of new software.
System statistical reliability model and analysis
NASA Technical Reports Server (NTRS)
Lekach, V. S.; Rood, H.
1973-01-01
A digital computer code was developed to simulate the time-dependent behavior of the 5-kwe reactor thermoelectric system. The code was used to determine lifetime sensitivity coefficients for a number of system design parameters, such as thermoelectric module efficiency and degradation rate, radiator absorptivity and emissivity, fuel element barrier defect constant, beginning-of-life reactivity, etc. A probability distribution (mean and standard deviation) was estimated for each of these design parameters. Then, error analysis was used to obtain a probability distribution for the system lifetime (mean = 7.7 years, standard deviation = 1.1 years). From this, the probability that the system will achieve the design goal of 5 years lifetime is 0.993. This value represents an estimate of the degradation reliability of the system.
The HONEYPOT Randomized Controlled Trial Statistical Analysis Plan
Pascoe, Elaine Mary; Lo, Serigne; Scaria, Anish; Badve, Sunil V.; Beller, Elaine Mary; Cass, Alan; Hawley, Carmel Mary; Johnson, David W.
2013-01-01
♦ Background: The HONEYPOT study is a multicenter, open-label, blinded-outcome, randomized controlled trial designed to determine whether, compared with standard topical application of mupirocin for nasal staphylococcal carriage, exit-site application of antibacterial honey reduces the rate of catheter-associated infections in peritoneal dialysis patients. ♦ Objective: To make public the pre-specified statistical analysis principles to be adhered to and the procedures to be performed by statisticians who will analyze the data for the HONEYPOT trial. ♦ Methods: Statisticians and clinical investigators who were blinded to treatment allocation and treatment-related study results and who will remain blinded until the central database is locked for final data extraction and analysis determined the statistical methods and procedures to be used for analysis and wrote the statistical analysis plan. The plan describes basic analysis principles, methods for dealing with a range of commonly encountered data analysis issues, and the specific statistical procedures for analyzing the primary, secondary, and safety outcomes. ♦ Results: A statistical analysis plan containing the pre-specified principles, methods, and procedures to be adhered to in the analysis of the data from the HONEYPOT trial was developed in accordance with international guidelines. The structure and content of the plan provide sufficient detail to meet the guidelines on statistical principles for clinical trials produced by the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use. ♦ Conclusions: Making public the pre-specified statistical analysis plan for the HONEYPOT trial minimizes the potential for bias in the analysis of trial data and the interpretation and reporting of trial results. PMID:23843589
Statistical Signal Analysis for Systems with Interferenced Inputs
NASA Technical Reports Server (NTRS)
Bai, R. M.; Mielnicka-Pate, A. L.
1985-01-01
A new approach is introduced, based on statistical signal analysis, which overcomes the error due to input signal interference. The model analyzed is given. The input signals u sub 1 (t) and u sub 2 (t) are assumed to be unknown. The measurable signals x sub 1 (t) and x sub 2 (t) are interferened according to the frequency response functions, H sub 12 (f) and H sub 21 (f). The goal of the analysis was to evaluate the power output due to each input, u sub 1 (t) and u sub 2 (t), for the case where both are applied to the same time. In addition, all frequency response functions are calculated. The interferenced system is described by a set of five equations with six unknown functions. An IBM XT Personal Computer, which was interfaced with the FFT, was used to solve the set of equations. The software was tested on an electrical two-input, one-output system. The results were excellent. The research presented includes the analysis of the acoustic radiation from a rectangular plate with two force inputs and the sound pressure as an output signal.
A Warning System for Stromboli Volcano Based on Statistical Analysis
NASA Astrophysics Data System (ADS)
Nunnari, Giuseppe; Puglisi, Giuseppe; Spata, Alessandro
2008-08-01
In this paper we describe a warning system based on statistical analysis for the purpose of monitoring ground deformation at the Sciara del Fuoco (Stromboli Volcano, Sicily). After a statistical analysis of ground deformation time-series measured at Stromboli by the monitoring system known as THEODOROS (THEOdolite and Distancemeter Robot Observatory of Stromboli), the paper describes the solution adopted for implementing the warning system. A robust statistical index has been defined in order to evaluate the movements of the area. A fuzzy approach has been proposed to evaluate an AI (Alarm Intensity) index which indicates the level of hazard of the Sciara del Fuoco sliding.
Statistical analysis of litter experiments in teratology
Williams, R.; Buschbom, R.L.
1982-11-01
Teratological data is binary response data (each fetus is either affected or not) in which the responses within a litter are usually not independent. As a result, the litter should be taken as the experimental unit. For each litter, its size, n, and the number of fetuses, x, possessing the effect of interest are recorded. The ratio p = x/n is then the basic data generated by the experiment. There are currently three general approaches to the analysis of teratological data: nonparametric, transformation followed by t-test or ANOVA, and parametric. The first two are currently in wide use by practitioners while the third is relatively new to the field. These first two also appear to possess comparable power levels while maintaining the nominal level of significance. When transformations are employed, care must be exercised to check that the transformed data has the required properties. Since the data is often highly asymmetric, there may be no transformation which renders the data nearly normal. The parametric procedures, including the beta-binomial model, offer the possibility of increased power.
A Divergence Statistics Extension to VTK for Performance Analysis.
Pebay, Philippe Pierre; Bennett, Janine Camille
2015-02-01
This report follows the series of previous documents ([PT08, BPRT09b, PT09, BPT09, PT10, PB13], where we presented the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k -means, order and auto-correlative statistics engines which we developed within the Visualization Tool Kit ( VTK ) as a scalable, parallel and versatile statistics package. We now report on a new engine which we developed for the calculation of divergence statistics, a concept which we hereafter explain and whose main goal is to quantify the discrepancy, in a stasticial manner akin to measuring a distance, between an observed empirical distribution and a theoretical, "ideal" one. The ease of use of the new diverence statistics engine is illustrated by the means of C++ code snippets. Although this new engine does not yet have a parallel implementation, it has already been applied to HPC performance analysis, of which we provide an example.
Data explorer: a prototype expert system for statistical analysis.
Aliferis, C.; Chao, E.; Cooper, G. F.
1993-01-01
The inadequate analysis of medical research data, due mainly to the unavailability of local statistical expertise, seriously jeopardizes the quality of new medical knowledge. Data Explorer is a prototype Expert System that builds on the versatility and power of existing statistical software, to provide automatic analyses and interpretation of medical data. The system draws much of its power by using belief network methods in place of more traditional, but difficult to automate, classical multivariate statistical techniques. Data Explorer identifies statistically significant relationships among variables, and using power-size analysis, belief network inference/learning and various explanatory techniques helps the user understand the importance of the findings. Finally the system can be used as a tool for the automatic development of predictive/diagnostic models from patient databases. PMID:8130501
A Statistical Analysis of the Charles F. Kettering Climate Scale.
ERIC Educational Resources Information Center
Johnson, William L.; Dixon, Paul N.
A statistical analysis was performed on the Charles F. Kettering (CFK) Scale, a popular four-section measure of school climate. The study centered on a multivariate analysis of Part A, the General Climate Factors section of the instrument, using data gathered from several elementary, junior high, and high school campuses in a large school district…
Analysis of Coastal Dunes: A Remote Sensing and Statistical Approach.
ERIC Educational Resources Information Center
Jones, J. Richard
1985-01-01
Remote sensing analysis and statistical methods were used to analyze the coastal dunes of Plum Island, Massachusetts. The research methodology used provides an example of a student project for remote sensing, geomorphology, or spatial analysis courses at the university level. (RM)
A statistical analysis of sea temperature data. A statistical analysis of sea temperature data
NASA Astrophysics Data System (ADS)
Lorentzen, Torbjørn
2015-02-01
The paper analyzes sea temperature series measured at two geographical locations along the coast of Norway. We address the question whether the series are stable over the sample period 1936-2012 and whether we can measure any signal of climate change in the regional data. We use nonstandard supF, OLS-based CUSUM, RE, and Chow tests in combination with the Bai-Perron's structural break test to identify potential changes in the temperature. The augmented Dickey-Fuller, the KPSS, and the nonparametric Phillips-Perron tests are in addition applied in the evaluation of the stochastic properties of the series. The analysis indicates that both series undergo similar structural instabilities in the form of small shifts in the temperature level. The temperature at Lista (58° 06' N, 06° 38' E) shifts downward about 1962 while the Skrova series (68° 12' N, 14° 10' E) shifts to a lower level about 1977. Both series shift upward about 1987, and after a period of increasing temperature, both series start leveling off about the turn of the millennium. The series have no significant stochastic or deterministic trend. The analysis indicates that the mean temperature has moved upward in decadal, small steps since the 1980s. The result is in accordance with recent analyses of sea temperatures in the North Atlantic. The findings are also related to the so-called hiatus phenomenon where natural variation in climate can mask global warming processes. The paper contributes to the discussion of applying objective methods in measuring climate change.
Fisher statistics for analysis of diffusion tensor directional information.
Hutchinson, Elizabeth B; Rutecki, Paul A; Alexander, Andrew L; Sutula, Thomas P
2012-04-30
A statistical approach is presented for the quantitative analysis of diffusion tensor imaging (DTI) directional information using Fisher statistics, which were originally developed for the analysis of vectors in the field of paleomagnetism. In this framework, descriptive and inferential statistics have been formulated based on the Fisher probability density function, a spherical analogue of the normal distribution. The Fisher approach was evaluated for investigation of rat brain DTI maps to characterize tissue orientation in the corpus callosum, fornix, and hilus of the dorsal hippocampal dentate gyrus, and to compare directional properties in these regions following status epilepticus (SE) or traumatic brain injury (TBI) with values in healthy brains. Direction vectors were determined for each region of interest (ROI) for each brain sample and Fisher statistics were applied to calculate the mean direction vector and variance parameters in the corpus callosum, fornix, and dentate gyrus of normal rats and rats that experienced TBI or SE. Hypothesis testing was performed by calculation of Watson's F-statistic and associated p-value giving the likelihood that grouped observations were from the same directional distribution. In the fornix and midline corpus callosum, no directional differences were detected between groups, however in the hilus, significant (p<0.0005) differences were found that robustly confirmed observations that were suggested by visual inspection of directionally encoded color DTI maps. The Fisher approach is a potentially useful analysis tool that may extend the current capabilities of DTI investigation by providing a means of statistical comparison of tissue structural orientation. PMID:22342971
Adaptive Strategy for the Statistical Analysis of Connectomes
Meskaldji, Djalel Eddine; Ottet, Marie-Christine; Cammoun, Leila; Hagmann, Patric; Meuli, Reto; Eliez, Stephan; Thiran, Jean Philippe; Morgenthaler, Stephan
2011-01-01
We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores. PMID:21829681
Data analysis using the Gnu R system for statistical computation
Simone, James; /Fermilab
2011-07-01
R is a language system for statistical computation. It is widely used in statistics, bioinformatics, machine learning, data mining, quantitative finance, and the analysis of clinical drug trials. Among the advantages of R are: it has become the standard language for developing statistical techniques, it is being actively developed by a large and growing global user community, it is open source software, it is highly portable (Linux, OS-X and Windows), it has a built-in documentation system, it produces high quality graphics and it is easily extensible with over four thousand extension library packages available covering statistics and applications. This report gives a very brief introduction to R with some examples using lattice QCD simulation results. It then discusses the development of R packages designed for chi-square minimization fits for lattice n-pt correlation functions.
Metrology Optical Power Budgeting in SIM Using Statistical Analysis Techniques
NASA Technical Reports Server (NTRS)
Kuan, Gary M
2008-01-01
The Space Interferometry Mission (SIM) is a space-based stellar interferometry instrument, consisting of up to three interferometers, which will be capable of micro-arc second resolution. Alignment knowledge of the three interferometer baselines requires a three-dimensional, 14-leg truss with each leg being monitored by an external metrology gauge. In addition, each of the three interferometers requires an internal metrology gauge to monitor the optical path length differences between the two sides. Both external and internal metrology gauges are interferometry based, operating at a wavelength of 1319 nanometers. Each gauge has fiber inputs delivering measurement and local oscillator (LO) power, split into probe-LO and reference-LO beam pairs. These beams experience power loss due to a variety of mechanisms including, but not restricted to, design efficiency, material attenuation, element misalignment, diffraction, and coupling efficiency. Since the attenuation due to these sources may degrade over time, an accounting of the range of expected attenuation is needed so an optical power margin can be book kept. A method of statistical optical power analysis and budgeting, based on a technique developed for deep space RF telecommunications, is described in this paper and provides a numerical confidence level for having sufficient optical power relative to mission metrology performance requirements.
Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint
Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad
2015-12-08
Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.
Plutonium metal exchange program : current status and statistical analysis
Tandon, L.; Eglin, J. L.; Michalak, S. E.; Picard, R. R.; Temer, D. J.
2004-01-01
The Rocky Flats Plutonium (Pu) Metal Sample Exchange program was conducted to insure the quality and intercomparability of measurements such as Pu assay, Pu isotopics, and impurity analyses. The Rocky Flats program was discontinued in 1989 after more than 30 years. In 2001, Los Alamos National Laboratory (LANL) reestablished the Pu Metal Exchange program. In addition to the Atomic Weapons Establishment (AWE) at Aldermaston, six Department of Energy (DOE) facilities Argonne East, Argonne West, Livermore, Los Alamos, New Brunswick Laboratory, and Savannah River are currently participating in the program. Plutonium metal samples are prepared and distributed to the sites for destructive measurements to determine elemental concentration, isotopic abundance, and both metallic and nonmetallic impurity levels. The program provides independent verification of analytical measurement capabilies for each participating facility and allows problems in analytical methods to be identified. The current status of the program will be discussed with emphasis on the unique statistical analysis and modeling of the data developed for the program. The discussion includes the definition of the consensus values for each analyte (in the presence and absence of anomalous values and/or censored values), and interesting features of the data and the results.
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
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
A κ-generalized statistical mechanics approach to income analysis
NASA Astrophysics Data System (ADS)
Clementi, F.; Gallegati, M.; Kaniadakis, G.
2009-02-01
This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low-middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful.
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.
NASA Technical Reports Server (NTRS)
Scargle, J. D.
1982-01-01
Detection of a periodic signal hidden in noise is frequently a goal in astronomical data analysis. This paper does not introduce a new detection technique, but instead studies the reliability and efficiency of detection with the most commonly used technique, the periodogram, in the case where the observation times are unevenly spaced. This choice was made because, of the methods in current use, it appears to have the simplest statistical behavior. A modification of the classical definition of the periodogram is necessary in order to retain the simple statistical behavior of the evenly spaced case. With this modification, periodogram analysis and least-squares fitting of sine waves to the data are exactly equivalent. Certain difficulties with the use of the periodogram are less important than commonly believed in the case of detection of strictly periodic signals. In addition, the standard method for mitigating these difficulties (tapering) can be used just as well if the sampling is uneven. An analysis of the statistical significance of signal detections is presented, with examples
Statistical Analysis of Tsunamis of the Italian Coasts
Caputo, M.; Faita, G.F.
1982-01-20
A study of a catalog of 138 tsunamis of the Italian coasts has been made. Intensitities of 106 tsunamis has been assigned and cataloged. The statistical analysis of this data fits a density distribution of the form log n = 3.00-0.425 I, where n is the number of tsunamis of intensity I per thousand years.
Revisiting the statistical analysis of pyroclast density and porosity data
NASA Astrophysics Data System (ADS)
Bernard, B.; Kueppers, U.; Ortiz, H.
2015-07-01
Explosive volcanic eruptions are commonly characterized based on a thorough analysis of the generated deposits. Amongst other characteristics in physical volcanology, density and porosity of juvenile clasts are some of the most frequently used to constrain eruptive dynamics. In this study, we evaluate the sensitivity of density and porosity data to statistical methods and introduce a weighting parameter to correct issues raised by the use of frequency analysis. Results of textural investigation can be biased by clast selection. Using statistical tools as presented here, the meaningfulness of a conclusion can be checked for any data set easily. This is necessary to define whether or not a sample has met the requirements for statistical relevance, i.e. whether a data set is large enough to allow for reproducible results. Graphical statistics are used to describe density and porosity distributions, similar to those used for grain-size analysis. This approach helps with the interpretation of volcanic deposits. To illustrate this methodology, we chose two large data sets: (1) directed blast deposits of the 3640-3510 BC eruption of Chachimbiro volcano (Ecuador) and (2) block-and-ash-flow deposits of the 1990-1995 eruption of Unzen volcano (Japan). We propose the incorporation of this analysis into future investigations to check the objectivity of results achieved by different working groups and guarantee the meaningfulness of the interpretation.
Statistical inference for exploratory data analysis and model diagnostics.
Buja, Andreas; Cook, Dianne; Hofmann, Heike; Lawrence, Michael; Lee, Eun-Kyung; Swayne, Deborah F; Wickham, Hadley
2009-11-13
We propose to furnish visual statistical methods with an inferential framework and protocol, modelled on confirmatory statistical testing. In this framework, plots take on the role of test statistics, and human cognition the role of statistical tests. Statistical significance of 'discoveries' is measured by having the human viewer compare the plot of the real dataset with collections of plots of simulated datasets. A simple but rigorous protocol that provides inferential validity is modelled after the 'lineup' popular from criminal legal procedures. Another protocol modelled after the 'Rorschach' inkblot test, well known from (pop-)psychology, will help analysts acclimatize to random variability before being exposed to the plot of the real data. The proposed protocols will be useful for exploratory data analysis, with reference datasets simulated by using a null assumption that structure is absent. The framework is also useful for model diagnostics in which case reference datasets are simulated from the model in question. This latter point follows up on previous proposals. Adopting the protocols will mean an adjustment in working procedures for data analysts, adding more rigour, and teachers might find that incorporating these protocols into the curriculum improves their students' statistical thinking. PMID:19805449
Statistical Software for spatial analysis of stratigraphic data sets
2003-04-08
Stratistics s a tool for statistical analysis of spatially explicit data sets and model output for description and for model-data comparisons. lt is intended for the analysis of data sets commonly used in geology, such as gamma ray logs and lithologic sequences, as well as 2-D data such as maps. Stratistics incorporates a far wider range of spatial analysis methods drawn from multiple disciplines, than are currently available in other packages. These include incorporation of techniques from spatial and landscape ecology, fractal analysis, and mathematical geology. Its use should substantially reduce the risk associated with the use of predictive models
Statistical Software for spatial analysis of stratigraphic data sets
Energy Science and Technology Software Center (ESTSC)
2003-04-08
Stratistics s a tool for statistical analysis of spatially explicit data sets and model output for description and for model-data comparisons. lt is intended for the analysis of data sets commonly used in geology, such as gamma ray logs and lithologic sequences, as well as 2-D data such as maps. Stratistics incorporates a far wider range of spatial analysis methods drawn from multiple disciplines, than are currently available in other packages. These include incorporation ofmore » techniques from spatial and landscape ecology, fractal analysis, and mathematical geology. Its use should substantially reduce the risk associated with the use of predictive models« less
Investigation of Weibull statistics in fracture analysis of cast aluminum
NASA Technical Reports Server (NTRS)
Holland, Frederic A., Jr.; Zaretsky, Erwin V.
1989-01-01
The fracture strengths of two large batches of A357-T6 cast aluminum coupon specimens were compared by using two-parameter Weibull analysis. The minimum number of these specimens necessary to find the fracture strength of the material was determined. The applicability of three-parameter Weibull analysis was also investigated. A design methodology based on the combination of elementary stress analysis and Weibull statistical analysis is advanced and applied to the design of a spherical pressure vessel shell. The results from this design methodology are compared with results from the applicable ASME pressure vessel code.
Hydrogeochemical characteristics of groundwater in Latvia using multivariate statistical analysis
NASA Astrophysics Data System (ADS)
Retike, Inga; Kalvans, Andis; Bikse, Janis; Popovs, Konrads; Babre, Alise
2015-04-01
product between the two previously named clusters. Groundwater in cluster 2, 6 and 7 is considered to be a result of carbonate weathering with some addition of sea salts or gypsum dissolution. As a conclusion, the highest or lowest concentrations of some trace elements in groundwater was found out to be strongly associated with certain clusters. For example, Cluster 9 represents gypsum dissolution and has the highest concentrations of F, Sr, Rb, Li and the lowest levels of Ba. It can be also concluded that multivariate statistical analysis of major components can be used as an exploratory and predictive tool to identify groundwater objects with high possibility of elevated or reduced concentrations of harmful or essential trace elements. The research is supported by the European Union through the ESF Mobilitas grant No MJD309 and the European Regional Development Fund project Nr.2013/0054/2DP/2.1.1.1.0/13/APIA/VIAA/007 and NRP project EVIDENnT project "Groundwater and climate scenarios" subproject "Groundwater Research".
Statistical analysis of flight times for space shuttle ferry flights
NASA Technical Reports Server (NTRS)
Graves, M. E.; Perlmutter, M.
1974-01-01
Markov chain and Monte Carlo analysis techniques are applied to the simulated Space Shuttle Orbiter Ferry flights to obtain statistical distributions of flight time duration between Edwards Air Force Base and Kennedy Space Center. The two methods are compared, and are found to be in excellent agreement. The flights are subjected to certain operational and meteorological requirements, or constraints, which cause eastbound and westbound trips to yield different results. Persistence of events theory is applied to the occurrence of inclement conditions to find their effect upon the statistical flight time distribution. In a sensitivity test, some of the constraints are varied to observe the corresponding changes in the results.
A novel statistical analysis and interpretation of flow cytometry data
Banks, H.T.; Kapraun, D.F.; Thompson, W. Clayton; Peligero, Cristina; Argilaguet, Jordi; Meyerhans, Andreas
2013-01-01
A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed. Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme. This scheme is then applied to experimental data for PHA-stimulated CD4+ T and CD8+ T cells collected from two healthy donors. This novel mathematical and statistical framework is shown to form the basis for accurate, meaningful analysis of cellular behaviour for a population of cells labelled with the dye carboxyfluorescein succinimidyl ester and stimulated to divide. PMID:23826744
Hayashi, C
1986-04-01
The subjects which are often encountered in the statistical design and analysis of data in medical science studies were discussed. The five topics examined were: Medical science and statistical methods So-called mathematical statistics and medical science Fundamentals of cross-tabulation analysis of statistical data and inference Exploratory study by multidimensional data analyses Optimal process control of individual, medical science and informatics of statistical data In I, the author's statistico-mathematical idea is characterized as the analysis of phenomena by statistical data. This is closely related to the logic, methodology and philosophy of science. This statistical concept and method are based on operational and pragmatic ideas. Self-examination of mathematical statistics is particularly focused in II and III. In II, the effectiveness of experimental design and statistical testing is thoroughly examined with regard to the study of medical science, and the limitation of its application is discussed. In III the apparent paradox of analysis of cross-tabulation of statistical data and statistical inference is shown. This is due to the operation of a simple two- or three-fold cross-tabulation analysis of (more than two or three) multidimensional data, apart from the sophisticated statistical test theory of association. In IV, the necessity of informatics of multidimensional data analysis in medical science is stressed. In V, the following point is discussed. The essential point of clinical trials is that they are not based on any simple statistical test in a traditional experimental design but on the optimal process control of individuals in the information space of the body and mind, which is based on a knowledge of medical science and the informatics of multidimensional statistical data analysis. PMID:3729436
Using Pre-Statistical Analysis to Streamline Monitoring Assessments
Reed, J.K.
1999-10-20
A variety of statistical methods exist to aid evaluation of groundwater quality and subsequent decision making in regulatory programs. These methods are applied because of large temporal and spatial extrapolations commonly applied to these data. In short, statistical conclusions often serve as a surrogate for knowledge. However, facilities with mature monitoring programs that have generated abundant data have inherently less uncertainty because of the sheer quantity of analytical results. In these cases, statistical tests can be less important, and ''expert'' data analysis should assume an important screening role.The WSRC Environmental Protection Department, working with the General Separations Area BSRI Environmental Restoration project team has developed a method for an Integrated Hydrogeological Analysis (IHA) of historical water quality data from the F and H Seepage Basins groundwater remediation project. The IHA combines common sense analytical techniques and a GIS presentation that force direct interactive evaluation of the data. The IHA can perform multiple data analysis tasks required by the RCRA permit. These include: (1) Development of a groundwater quality baseline prior to remediation startup, (2) Targeting of constituents for removal from RCRA GWPS, (3) Targeting of constituents for removal from UIC, permit, (4) Targeting of constituents for reduced, (5)Targeting of monitoring wells not producing representative samples, (6) Reduction in statistical evaluation, and (7) Identification of contamination from other facilities.
Multivariate statistical analysis of atom probe tomography data
Parish, Chad M; Miller, Michael K
2010-01-01
The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed.
Multivariate statistical analysis of atom probe tomography data.
Parish, Chad M; Miller, Michael K
2010-10-01
The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed. PMID:20650566
Feature-based statistical analysis of combustion simulation data.
Bennett, Janine C; Krishnamoorthy, Vaidyanathan; Liu, Shusen; Grout, Ray W; Hawkes, Evatt R; Chen, Jacqueline H; Shepherd, Jason; Pascucci, Valerio; Bremer, Peer-Timo
2011-12-01
We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion
Feature-Based Statistical Analysis of Combustion Simulation Data
Bennett, J; Krishnamoorthy, V; Liu, S; Grout, R; Hawkes, E; Chen, J; Pascucci, V; Bremer, P T
2011-11-18
We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion
Computed Tomography Inspection and Analysis for Additive Manufacturing Components
NASA Technical Reports Server (NTRS)
Beshears, Ronald D.
2016-01-01
Computed tomography (CT) inspection was performed on test articles additively manufactured from metallic materials. Metallic AM and machined wrought alloy test articles with programmed flaws were inspected using a 2MeV linear accelerator based CT system. Performance of CT inspection on identically configured wrought and AM components and programmed flaws was assessed using standard image analysis techniques to determine the impact of additive manufacturing on inspectability of objects with complex geometries.
HistFitter - A flexible framework for statistical data analysis
NASA Astrophysics Data System (ADS)
Lorenz, J. M.; Baak, M.; Besjes, G. J.; Côté, D.; Koutsman, A.; Short, D.
2015-05-01
We present a software framework for statistical data analysis, called HistFitter, that has extensively been used in the ATLAS Collaboration to analyze data of proton-proton collisions produced by the Large Hadron Collider at CERN. Most notably, HistFitter has become a de-facto standard in searches for supersymmetric particles since 2012, with some usage for Exotic and Higgs boson physics. HistFitter coherently combines several statistics tools in a programmable and flexible framework that is capable of bookkeeping hundreds of data models under study using thousands of generated input histograms. The key innovations of HistFitter are to weave the concepts of control, validation and signal regions into its very fabric, and to treat them with rigorous statistical methods, while providing multiple tools to visualize and interpret the results through a simple configuration interface.
SMART: Statistical Metabolomics Analysis-An R Tool.
Liang, Yu-Jen; Lin, Yu-Ting; Chen, Chia-Wei; Lin, Chien-Wei; Chao, Kun-Mao; Pan, Wen-Harn; Yang, Hsin-Chou
2016-06-21
Metabolomics data provide unprecedented opportunities to decipher metabolic mechanisms by analyzing hundreds to thousands of metabolites. Data quality concerns and complex batch effects in metabolomics must be appropriately addressed through statistical analysis. This study developed an integrated analysis tool for metabolomics studies to streamline the complete analysis flow from initial data preprocessing to downstream association analysis. We developed Statistical Metabolomics Analysis-An R Tool (SMART), which can analyze input files with different formats, visually represent various types of data features, implement peak alignment and annotation, conduct quality control for samples and peaks, explore batch effects, and perform association analysis. A pharmacometabolomics study of antihypertensive medication was conducted and data were analyzed using SMART. Neuromedin N was identified as a metabolite significantly associated with angiotensin-converting-enzyme inhibitors in our metabolome-wide association analysis (p = 1.56 × 10(-4) in an analysis of covariance (ANCOVA) with an adjustment for unknown latent groups and p = 1.02 × 10(-4) in an ANCOVA with an adjustment for hidden substructures). This endogenous neuropeptide is highly related to neurotensin and neuromedin U, which are involved in blood pressure regulation and smooth muscle contraction. The SMART software, a user guide, and example data can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/metabolomics/SMART.htm . PMID:27248514
2008-01-01
There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study. PMID:18765754
3D statistical failure analysis of monolithic dental ceramic crowns.
Nasrin, Sadia; Katsube, Noriko; Seghi, Robert R; Rokhlin, Stanislav I
2016-07-01
For adhesively retained ceramic crown of various types, it has been clinically observed that the most catastrophic failures initiate from the cement interface as a result of radial crack formation as opposed to Hertzian contact stresses originating on the occlusal surface. In this work, a 3D failure prognosis model is developed for interface initiated failures of monolithic ceramic crowns. The surface flaw distribution parameters determined by biaxial flexural tests on ceramic plates and point-to-point variations of multi-axial stress state at the intaglio surface are obtained by finite element stress analysis. They are combined on the basis of fracture mechanics based statistical failure probability model to predict failure probability of a monolithic crown subjected to single-cycle indentation load. The proposed method is verified by prior 2D axisymmetric model and experimental data. Under conditions where the crowns are completely bonded to the tooth substrate, both high flexural stress and high interfacial shear stress are shown to occur in the wall region where the crown thickness is relatively thin while high interfacial normal tensile stress distribution is observed at the margin region. Significant impact of reduced cement modulus on these stress states is shown. While the analyses are limited to single-cycle load-to-failure tests, high interfacial normal tensile stress or high interfacial shear stress may contribute to degradation of the cement bond between ceramic and dentin. In addition, the crown failure probability is shown to be controlled by high flexural stress concentrations over a small area, and the proposed method might be of some value to detect initial crown design errors. PMID:27215334
Revisiting the statistical analysis of pyroclast density and porosity data
NASA Astrophysics Data System (ADS)
Bernard, B.; Kueppers, U.; Ortiz, H.
2015-03-01
Explosive volcanic eruptions are commonly characterized based on a thorough analysis of the generated deposits. Amongst other characteristics in physical volcanology, density and porosity of juvenile clasts are some of the most frequently used characteristics to constrain eruptive dynamics. In this study, we evaluate the sensitivity of density and porosity data and introduce a weighting parameter to correct issues raised by the use of frequency analysis. Results of textural investigation can be biased by clast selection. Using statistical tools as presented here, the meaningfulness of a conclusion can be checked for any dataset easily. This is necessary to define whether or not a sample has met the requirements for statistical relevance, i.e. whether a dataset is large enough to allow for reproducible results. Graphical statistics are used to describe density and porosity distributions, similar to those used for grain-size analysis. This approach helps with the interpretation of volcanic deposits. To illustrate this methodology we chose two large datasets: (1) directed blast deposits of the 3640-3510 BC eruption of Chachimbiro volcano (Ecuador) and (2) block-and-ash-flow deposits of the 1990-1995 eruption of Unzen volcano (Japan). We propose add the use of this analysis for future investigations to check the objectivity of results achieved by different working groups and guarantee the meaningfulness of the interpretation.
Statistical Analysis of the Heavy Neutral Atoms Measured by IBEX
NASA Astrophysics Data System (ADS)
Park, Jeewoo; Kucharek, Harald; Möbius, Eberhard; Galli, André; Livadiotis, George; Fuselier, Steve A.; McComas, David J.
2015-10-01
We investigate the directional distribution of heavy neutral atoms in the heliosphere by using heavy neutral maps generated with the IBEX-Lo instrument over three years from 2009 to 2011. The interstellar neutral (ISN) O&Ne gas flow was found in the first-year heavy neutral map at 601 keV and its flow direction and temperature were studied. However, due to the low counting statistics, researchers have not treated the full sky maps in detail. The main goal of this study is to evaluate the statistical significance of each pixel in the heavy neutral maps to get a better understanding of the directional distribution of heavy neutral atoms in the heliosphere. Here, we examine three statistical analysis methods: the signal-to-noise filter, the confidence limit method, and the cluster analysis method. These methods allow us to exclude background from areas where the heavy neutral signal is statistically significant. These methods also allow the consistent detection of heavy neutral atom structures. The main emission feature expands toward lower longitude and higher latitude from the observational peak of the ISN O&Ne gas flow. We call this emission the extended tail. It may be an imprint of the secondary oxygen atoms generated by charge exchange between ISN hydrogen atoms and oxygen ions in the outer heliosheath.
Statistical analysis of spectral data for vegetation detection
NASA Astrophysics Data System (ADS)
Love, Rafael; Cathcart, J. Michael
2006-05-01
Identification and reduction of false alarms provide a critical component in the detection of landmines. Research at Georgia Tech over the past several years has focused on this problem through an examination of the signature characteristics of various background materials. These efforts seek to understand the physical basis and features of these signatures as an aid to the development of false target identification techniques. The investigation presented in this paper deal concentrated on the detection of foliage in long wave infrared imagery. Data collected by a hyperspectral long-wave infrared sensor provided the background signatures used in this study. These studies focused on an analysis of the statistical characteristics of both the intensity signature and derived emissivity data. Results from these studies indicate foliage signatures possess unique characteristics that can be exploited to enable detection of vegetation in LWIR images. This paper will present review of the approach and results of the statistical analysis.
Statistical analysis of mineral soils in the Odra valley
NASA Astrophysics Data System (ADS)
Hudak, Magda; Rojna, Arkadiusz
2012-10-01
The aim of this article is to present the results of statistical analyses of laboratory experiment results obtained from an ITB ZW-K2 apparatus, Kamieński tubes and grain-size distribution curves. Beside basic statistical parameters (mean, sum, minimum and maximum), correlation analysis and multivariate analysis of variance at significance levels α < 0.01 and α < 0.05 were taken into account, as well as calculations of LSD confidence half-intervals. The research material was collected from the valley of the Odra river near the town of Słubice in Lubuskie province. The research involved mineral, non-rock fine-grained, non-cohesive soils lying at the depth of 0.3-1.5 m.
A statistical analysis of mesoscale rainfall as a random cascade
NASA Technical Reports Server (NTRS)
Gupta, Vijay K.; Waymire, Edward C.
1993-01-01
The paper considers the random cascade theory for spatial rainfall. Particular attention was given to the following four areas: (1) the relationship of the random cascade theory of rainfall to the simple scaling and the hierarchical cluster-point-process theories, (2) the mathematical foundations for some of the formalisms commonly applied in the develpment of statistical cascade theory, (3) the empirical evidence for a random cascade theory of rainfall, and (4) the way of using data for making estimates of parameters and for making statistical inference within this theoretical framework. An analysis of space-time rainfall data is presented. Cascade simulations are carried out to provide a comparison with methods of analysis that are applied to the rainfall data.
Statistical Analysis of speckle noise reduction techniques for echocardiographic Images
NASA Astrophysics Data System (ADS)
Saini, Kalpana; Dewal, M. L.; Rohit, Manojkumar
2011-12-01
Echocardiography is the safe, easy and fast technology for diagnosing the cardiac diseases. As in other ultrasound images these images also contain speckle noise. In some cases this speckle noise is useful such as in motion detection. But in general noise removal is required for better analysis of the image and proper diagnosis. Different Adaptive and anisotropic filters are included for statistical analysis. Statistical parameters such as Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE) calculated for performance measurement. One more important aspect that there may be blurring during speckle noise removal. So it is prefered that filter should be able to enhance edges during noise removal.
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…
Lifetime statistics of quantum chaos studied by a multiscale analysis
Di Falco, A.; Krauss, T. F.; Fratalocchi, A.
2012-04-30
In a series of pump and probe experiments, we study the lifetime statistics of a quantum chaotic resonator when the number of open channels is greater than one. Our design embeds a stadium billiard into a two dimensional photonic crystal realized on a silicon-on-insulator substrate. We calculate resonances through a multiscale procedure that combines energy landscape analysis and wavelet transforms. Experimental data is found to follow the universal predictions arising from random matrix theory with an excellent level of agreement.
Statistical sampling analysis for stratospheric measurements from satellite missions
NASA Technical Reports Server (NTRS)
Drewry, J. W.; Harrison, E. F.; Brooks, D. R.; Robbins, J. L.
1978-01-01
Earth orbiting satellite experiments can be designed to measure stratospheric constituents such as ozone by utilizing remote sensing techniques. Statistical analysis techniques, mission simulation and model development have been utilized to develop a method for analyzing various mission/sensor combinations. Existing and planned NASA satellite missions such as Nimbus-4 and G, and Stratospheric Aerosol and Gas Experiment-Application Explorer Mission (SAGE-AEM) have been analyzed to determine the ability of the missions to adequately sample the global field.
Statistical Analysis of the Exchange Rate of Bitcoin.
Chu, Jeffrey; Nadarajah, Saralees; Chan, Stephen
2015-01-01
Bitcoin, the first electronic payment system, is becoming a popular currency. We provide a statistical analysis of the log-returns of the exchange rate of Bitcoin versus the United States Dollar. Fifteen of the most popular parametric distributions in finance are fitted to the log-returns. The generalized hyperbolic distribution is shown to give the best fit. Predictions are given for future values of the exchange rate. PMID:26222702
Statistical Analysis of the Exchange Rate of Bitcoin
Chu, Jeffrey; Nadarajah, Saralees; Chan, Stephen
2015-01-01
Bitcoin, the first electronic payment system, is becoming a popular currency. We provide a statistical analysis of the log-returns of the exchange rate of Bitcoin versus the United States Dollar. Fifteen of the most popular parametric distributions in finance are fitted to the log-returns. The generalized hyperbolic distribution is shown to give the best fit. Predictions are given for future values of the exchange rate. PMID:26222702
The statistical analysis of multivariate serological frequency data.
Reyment, Richard A
2005-11-01
Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject. PMID:16024067
Statistical Analysis of Longitudinal Psychiatric Data with Dropouts
Mazumdar, Sati; Tang, Gong; Houck, Patricia R.; Dew, Mary Amanda; Begley, Amy E.; Scott, John; Mulsant, Benoit H.; Reynolds, Charles F.
2007-01-01
Longitudinal studies are used in psychiatric research to address outcome changes over time within and between individuals. However, because participants may drop out of a study prematurely, ignoring the nature of dropout often leads to biased inference, and in turn, wrongful conclusions. The purpose of the present paper is: (1) to review several dropout processes, corresponding inferential issues and recent methodological advances; (2) to evaluate the impact of assumptions regarding the dropout processes on inference by simulation studies and an illustrative example using psychiatric data; and (3) to provide a general strategy for practitioners to perform analyses of longitudinal data with dropouts, using software available commercially or in the public domain. The statistical methods used in this paper are maximum likelihood, multiple imputation and semi-parametric regression methods for inference, as well as Little’s test and ISNI (Index of Sensitivity to Nonignorability) for assessing statistical dropout mechanisms. We show that accounting for the nature of the dropout process influences results and that sensitivity analysis is useful in assessing the robustness of parameter estimates and related uncertainties. We conclude that recording the causes of dropouts should be an integral part of any statistical analysis with longitudinal psychiatric data, and we recommend performing a sensitivity analysis when the exact nature of the dropout process cannot be discerned. PMID:17092516
Ambiguity and nonidentifiability in the statistical analysis of neural codes.
Amarasingham, Asohan; Geman, Stuart; Harrison, Matthew T
2015-05-19
Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, "Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?" For another example, "How much of a neuron's observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?" However, a neuron's theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature. PMID:25934918
Common misconceptions about data analysis and statistics1
Motulsky, Harvey J
2015-01-01
Ideally, any experienced investigator with the right tools should be able to reproduce a finding published in a peer-reviewed biomedical science journal. In fact, the reproducibility of a large percentage of published findings has been questioned. Undoubtedly, there are many reasons for this, but one reason may be that investigators fool themselves due to a poor understanding of statistical concepts. In particular, investigators often make these mistakes: (1) P-Hacking. This is when you reanalyze a data set in many different ways, or perhaps reanalyze with additional replicates, until you get the result you want. (2) Overemphasis on P values rather than on the actual size of the observed effect. (3) Overuse of statistical hypothesis testing, and being seduced by the word “significant”. (4) Overreliance on standard errors, which are often misunderstood. PMID:25692012
Spectral Analysis of B Stars: An Application of Bayesian Statistics
NASA Astrophysics Data System (ADS)
Mugnes, J.-M.; Robert, C.
2012-12-01
To better understand the processes involved in stellar physics, it is necessary to obtain accurate stellar parameters (effective temperature, surface gravity, abundances…). Spectral analysis is a powerful tool for investigating stars, but it is also vital to reduce uncertainties at a decent computational cost. Here we present a spectral analysis method based on a combination of Bayesian statistics and grids of synthetic spectra obtained with TLUSTY. This method simultaneously constrains the stellar parameters by using all the lines accessible in observed spectra and thus greatly reduces uncertainties and improves the overall spectrum fitting. Preliminary results are shown using spectra from the Observatoire du Mont-Mégantic.
A Laboratory Exercise in Statistical Analysis of Data
NASA Astrophysics Data System (ADS)
Vitha, Mark F.; Carr, Peter W.
1997-08-01
An undergraduate laboratory exercise in statistical analysis of data has been developed based on facile weighings of vitamin E pills. The use of electronic top-loading balances allows for very rapid data collection. Therefore, students obtain a sufficiently large number of replicates to provide statistically meaningful data sets. Through this exercise, students explore the effects of sample size and different types of sample averaging on the standard deviation of the average weight per pill. An emphasis is placed on the difference between the standard deviation of the mean and the standard deviation of the population. Students also perform the Q-test and t-test and are introduced to the X2-test. In this report, the class data from two consecutive offerings of the course are compared and reveal a statistically significant increase in the average weight per pill, presumably due to the absorption of water over time. Histograms of the class data are shown and used to illustrate the importance of plotting the data. Overall, through this brief laboratory exercise, students are exposed to many important statistical tests and concepts which are then used and further developed throughout the remainder of the course.
HistFitter: a flexible framework for statistical data analysis
NASA Astrophysics Data System (ADS)
Besjes, G. J.; Baak, M.; Côté, D.; Koutsman, A.; Lorenz, J. M.; Short, D.
2015-12-01
HistFitter is a software framework for statistical data analysis that has been used extensively in the ATLAS Collaboration to analyze data of proton-proton collisions produced by the Large Hadron Collider at CERN. Most notably, HistFitter has become a de-facto standard in searches for supersymmetric particles since 2012, with some usage for Exotic and Higgs boson physics. HistFitter coherently combines several statistics tools in a programmable and flexible framework that is capable of bookkeeping hundreds of data models under study using thousands of generated input histograms. HistFitter interfaces with the statistics tools HistFactory and RooStats to construct parametric models and to perform statistical tests of the data, and extends these tools in four key areas. The key innovations are to weave the concepts of control, validation and signal regions into the very fabric of HistFitter, and to treat these with rigorous methods. Multiple tools to visualize and interpret the results through a simple configuration interface are also provided.
Region-based Statistical Analysis of 2D PAGE Images
Li, Feng; Seillier-Moiseiwitsch, Françoise; Korostyshevskiy, Valeriy R.
2011-01-01
A new comprehensive procedure for statistical analysis of two-dimensional polyacrylamide gel electrophoresis (2D PAGE) images is proposed, including protein region quantification, normalization and statistical analysis. Protein regions are defined by the master watershed map that is obtained from the mean gel. By working with these protein regions, the approach bypasses the current bottleneck in the analysis of 2D PAGE images: it does not require spot matching. Background correction is implemented in each protein region by local segmentation. Two-dimensional locally weighted smoothing (LOESS) is proposed to remove any systematic bias after quantification of protein regions. Proteins are separated into mutually independent sets based on detected correlations, and a multivariate analysis is used on each set to detect the group effect. A strategy for multiple hypothesis testing based on this multivariate approach combined with the usual Benjamini-Hochberg FDR procedure is formulated and applied to the differential analysis of 2D PAGE images. Each step in the analytical protocol is shown by using an actual dataset. The effectiveness of the proposed methodology is shown using simulated gels in comparison with the commercial software packages PDQuest and Dymension. We also introduce a new procedure for simulating gel images. PMID:21850152
Statistical analysis of heartbeat data with wavelet techniques
NASA Astrophysics Data System (ADS)
Pazsit, Imre
2004-05-01
The purpose of this paper is to demonstrate the use of some methods of signal analysis, performed on ECG and in some cases blood pressure signals, for the classification of the health status of the heart of mice and rats. Spectral and wavelet analysis were performed on the raw signals. FFT-based coherence and phase was also calculated between blood pressure and raw ECG signals. Finally, RR-intervals were deduced from the ECG signals and an analysis of the fractal dimensions was performed. The analysis was made on data from mice and rats. A correlation was found between the health status of the mice and the rats and some of the statistical descriptors, most notably the phase of the cross-spectra between ECG and blood pressure, and the fractal properties and dimensions of the interbeat series (RR-interval fluctuations).
Agriculture, population growth, and statistical analysis of the radiocarbon record
Zahid, H. Jabran; Robinson, Erick; Kelly, Robert L.
2016-01-01
The human population has grown significantly since the onset of the Holocene about 12,000 y ago. Despite decades of research, the factors determining prehistoric population growth remain uncertain. Here, we examine measurements of the rate of growth of the prehistoric human population based on statistical analysis of the radiocarbon record. We find that, during most of the Holocene, human populations worldwide grew at a long-term annual rate of 0.04%. Statistical analysis of the radiocarbon record shows that transitioning farming societies experienced the same rate of growth as contemporaneous foraging societies. The same rate of growth measured for populations dwelling in a range of environments and practicing a variety of subsistence strategies suggests that the global climate and/or endogenous biological factors, not adaptability to local environment or subsistence practices, regulated the long-term growth of the human population during most of the Holocene. Our results demonstrate that statistical analyses of large ensembles of radiocarbon dates are robust and valuable for quantitatively investigating the demography of prehistoric human populations worldwide. PMID:26699457
Agriculture, population growth, and statistical analysis of the radiocarbon record.
Zahid, H Jabran; Robinson, Erick; Kelly, Robert L
2016-01-26
The human population has grown significantly since the onset of the Holocene about 12,000 y ago. Despite decades of research, the factors determining prehistoric population growth remain uncertain. Here, we examine measurements of the rate of growth of the prehistoric human population based on statistical analysis of the radiocarbon record. We find that, during most of the Holocene, human populations worldwide grew at a long-term annual rate of 0.04%. Statistical analysis of the radiocarbon record shows that transitioning farming societies experienced the same rate of growth as contemporaneous foraging societies. The same rate of growth measured for populations dwelling in a range of environments and practicing a variety of subsistence strategies suggests that the global climate and/or endogenous biological factors, not adaptability to local environment or subsistence practices, regulated the long-term growth of the human population during most of the Holocene. Our results demonstrate that statistical analyses of large ensembles of radiocarbon dates are robust and valuable for quantitatively investigating the demography of prehistoric human populations worldwide. PMID:26699457
Statistical wind analysis for near-space applications
NASA Astrophysics Data System (ADS)
Roney, Jason A.
2007-09-01
Statistical wind models were developed based on the existing observational wind data for near-space altitudes between 60 000 and 100 000 ft (18 30 km) above ground level (AGL) at two locations, Akon, OH, USA, and White Sands, NM, USA. These two sites are envisioned as playing a crucial role in the first flights of high-altitude airships. The analysis shown in this paper has not been previously applied to this region of the stratosphere for such an application. Standard statistics were compiled for these data such as mean, median, maximum wind speed, and standard deviation, and the data were modeled with Weibull distributions. These statistics indicated, on a yearly average, there is a lull or a “knee” in the wind between 65 000 and 72 000 ft AGL (20 22 km). From the standard statistics, trends at both locations indicated substantial seasonal variation in the mean wind speed at these heights. The yearly and monthly statistical modeling indicated that Weibull distributions were a reasonable model for the data. Forecasts and hindcasts were done by using a Weibull model based on 2004 data and comparing the model with the 2003 and 2005 data. The 2004 distribution was also a reasonable model for these years. Lastly, the Weibull distribution and cumulative function were used to predict the 50%, 95%, and 99% winds, which are directly related to the expected power requirements of a near-space station-keeping airship. These values indicated that using only the standard deviation of the mean may underestimate the operational conditions.
Analysis of the Spatial Organization of Molecules with Robust Statistics
Lagache, Thibault; Lang, Gabriel; Sauvonnet, Nathalie; Olivo-Marin, Jean-Christophe
2013-01-01
One major question in molecular biology is whether the spatial distribution of observed molecules is random or organized in clusters. Indeed, this analysis gives information about molecules’ interactions and physical interplay with their environment. The standard tool for analyzing molecules’ distribution statistically is the Ripley’s K function, which tests spatial randomness through the computation of its critical quantiles. However, quantiles’ computation is very cumbersome, hindering its use. Here, we present an analytical expression of these quantiles, leading to a fast and robust statistical test, and we derive the characteristic clusters’ size from the maxima of the Ripley’s K function. Subsequently, we analyze the spatial organization of endocytic spots at the cell membrane and we report that clathrin spots are randomly distributed while clathrin-independent spots are organized in clusters with a radius of , which suggests distinct physical mechanisms and cellular functions for each pathway. PMID:24349021
Statistical analysis of nanoparticle dosing in a dynamic cellular system
NASA Astrophysics Data System (ADS)
Summers, Huw D.; Rees, Paul; Holton, Mark D.; Rowan Brown, M.; Chappell, Sally C.; Smith, Paul J.; Errington, Rachel J.
2011-03-01
The delivery of nanoparticles into cells is important in therapeutic applications and in nanotoxicology. Nanoparticles are generally targeted to receptors on the surfaces of cells and internalized into endosomes by endocytosis, but the kinetics of the process and the way in which cell division redistributes the particles remain unclear. Here we show that the chance of success or failure of nanoparticle uptake and inheritance is random. Statistical analysis of nanoparticle-loaded endosomes indicates that particle capture is described by an over-dispersed Poisson probability distribution that is consistent with heterogeneous adsorption and internalization. Partitioning of nanoparticles in cell division is random and asymmetric, following a binomial distribution with mean probability of 0.52-0.72. These results show that cellular targeting of nanoparticles is inherently imprecise due to the randomness of nature at the molecular scale, and the statistical framework offers a way to predict nanoparticle dosage for therapy and for the study of nanotoxins.
Statistical analysis of the particulation of shaped charge jets
Minich, R W, Baker, E L; Schwartz, A J
1999-08-12
A statistical analysis of shaped charge jet break-up was carried out in order to investigate the role of nonlinear instabilities leading to the particulation of the jet. Statistical methods generally used for studying fluctuations in nonlinear dynamical systems are applied to experimentally measured velocities of the individual particles. In particular we present results suggesting the deviation of non-Gaussian behavior for interparticle velocity correlations, characteristic of nonlinear dynamical systems. Results are presented for two silver shaped charge jets that differ primarily in their material processing. We provide evidence that the particulation of a jet is not random, but has its origin in a deterministic dynamical process involving the nonlinear coupling of two oscillators analogous to the underling dynamics observed in Rayleigh-Benard convection and modeled in the return map of Curry and Yorke.
A Statistical Analysis of Lunisolar-Earthquake Connections
NASA Astrophysics Data System (ADS)
Rüegg, Christian Michael-André
2012-11-01
Despite over a century of study, the relationship between lunar cycles and earthquakes remains controversial and difficult to quantitatively investigate. Perhaps as a consequence, major earthquakes around the globe are frequently followed by "prediction claim", using lunar cycles, that generate media furore and pressure scientists to provide resolute answers. The 2010-2011 Canterbury earthquakes in New Zealand were no exception; significant media attention was given to lunar derived earthquake predictions by non-scientists, even though the predictions were merely "opinions" and were not based on any statistically robust temporal or causal relationships. This thesis provides a framework for studying lunisolar earthquake temporal relationships by developing replicable statistical methodology based on peer reviewed literature. Notable in the methodology is a high accuracy ephemeris, called ECLPSE, designed specifically by the author for use on earthquake catalogs and a model for performing phase angle analysis.
Statistical analysis of effective singular values in matrix rank determination
NASA Technical Reports Server (NTRS)
Konstantinides, Konstantinos; Yao, Kung
1988-01-01
A major problem in using SVD (singular-value decomposition) as a tool in determining the effective rank of a perturbed matrix is that of distinguishing between significantly small and significantly large singular values to the end, conference regions are derived for the perturbed singular values of matrices with noisy observation data. The analysis is based on the theories of perturbations of singular values and statistical significance test. Threshold bounds for perturbation due to finite-precision and i.i.d. random models are evaluated. In random models, the threshold bounds depend on the dimension of the matrix, the noisy variance, and predefined statistical level of significance. Results applied to the problem of determining the effective order of a linear autoregressive system from the approximate rank of a sample autocorrelation matrix are considered. Various numerical examples illustrating the usefulness of these bounds and comparisons to other previously known approaches are given.
Statistical analysis of nanoparticle dosing in a dynamic cellular system.
Summers, Huw D; Rees, Paul; Holton, Mark D; Brown, M Rowan; Chappell, Sally C; Smith, Paul J; Errington, Rachel J
2011-03-01
The delivery of nanoparticles into cells is important in therapeutic applications and in nanotoxicology. Nanoparticles are generally targeted to receptors on the surfaces of cells and internalized into endosomes by endocytosis, but the kinetics of the process and the way in which cell division redistributes the particles remain unclear. Here we show that the chance of success or failure of nanoparticle uptake and inheritance is random. Statistical analysis of nanoparticle-loaded endosomes indicates that particle capture is described by an over-dispersed Poisson probability distribution that is consistent with heterogeneous adsorption and internalization. Partitioning of nanoparticles in cell division is random and asymmetric, following a binomial distribution with mean probability of 0.52-0.72. These results show that cellular targeting of nanoparticles is inherently imprecise due to the randomness of nature at the molecular scale, and the statistical framework offers a way to predict nanoparticle dosage for therapy and for the study of nanotoxins. PMID:21258333
Statistical analysis of subjective preferences for video enhancement
NASA Astrophysics Data System (ADS)
Woods, Russell L.; Satgunam, PremNandhini; Bronstad, P. Matthew; Peli, Eli
2010-02-01
Measuring preferences for moving video quality is harder than for static images due to the fleeting and variable nature of moving video. Subjective preferences for image quality can be tested by observers indicating their preference for one image over another. Such pairwise comparisons can be analyzed using Thurstone scaling (Farrell, 1999). Thurstone (1927) scaling is widely used in applied psychology, marketing, food tasting and advertising research. Thurstone analysis constructs an arbitrary perceptual scale for the items that are compared (e.g. enhancement levels). However, Thurstone scaling does not determine the statistical significance of the differences between items on that perceptual scale. Recent papers have provided inferential statistical methods that produce an outcome similar to Thurstone scaling (Lipovetsky and Conklin, 2004). Here, we demonstrate that binary logistic regression can analyze preferences for enhanced video.
Noise removing in encrypted color images by statistical analysis
NASA Astrophysics Data System (ADS)
Islam, N.; Puech, W.
2012-03-01
Cryptographic techniques are used to secure confidential data from unauthorized access but these techniques are very sensitive to noise. A single bit change in encrypted data can have catastrophic impact over the decrypted data. This paper addresses the problem of removing bit error in visual data which are encrypted using AES algorithm in the CBC mode. In order to remove the noise, a method is proposed which is based on the statistical analysis of each block during the decryption. The proposed method exploits local statistics of the visual data and confusion/diffusion properties of the encryption algorithm to remove the errors. Experimental results show that the proposed method can be used at the receiving end for the possible solution for noise removing in visual data in encrypted domain.
Statistical Mechanics Analysis of ATP Binding to a Multisubunit Enzyme
NASA Astrophysics Data System (ADS)
Zhang, Yun-Xin
2014-10-01
Due to inter-subunit communication, multisubunit enzymes usually hydrolyze ATP in a concerted fashion. However, so far the principle of this process remains poorly understood. In this study, from the viewpoint of statistical mechanics, a simple model is presented. In this model, we assume that the binding of ATP will change the potential of the corresponding enzyme subunit, and the degree of this change depends on the state of its adjacent subunits. The probability of enzyme in a given state satisfies the Boltzmann's distribution. Although it looks much simple, this model can fit the recent experimental data of chaperonin TRiC/CCT well. From this model, the dominant state of TRiC/CCT can be obtained. This study provide a new way to understand biophysical processe by statistical mechanics analysis.
Statistical analysis of the modal properties of large structural systems.
NASA Technical Reports Server (NTRS)
Collins, J. D.; Kennedy, B.; Hart, G. C.
1971-01-01
A theory is developed to predict eigenvalue and eigenvector uncertainty in large dynamic models. The uncertainty is based on physical property uncertainty and should not be confused with numerical roundoff, although the method can be extended to include the latter. The theory, when implemented on a computer, is used to analyze the uncertainties in frequencies and mode shapes based on uncertainties in mass, stiffness, modulus of elasticity, etc. The method incorporates a linear statistical model which is quite adequate for handling property uncertainties of 10% or more. The model is not limited to small systems but uses certain statistical assumptions as well as selective matrix manipulations to keep the size of all matrix operations to within the number of degrees of freedom of the system. Examples are given for two longitudinal vibration problems, and the results are supported by a Monte Carlo analysis.
Statistical Analysis of Human Blood Cytometries: Potential Donors and Patients
NASA Astrophysics Data System (ADS)
Bernal-Alvarado, J.; Segovia-Olvera, P.; Mancilla-Escobar, B. E.; Palomares, P.
2004-09-01
The histograms of the cell volume from human blood present valuable information for clinical evaluation. Measurements can be performed with automatic equipment and a graphical presentation of the data is available, nevertheless, an statistical and mathematical analysis of the cell volume distribution could be useful for medical interpretation too, as much as the numerical parameters characterizing the histograms might be correlated with healthy people and patient populations. In this work, a statistical exercise was performed in order to find the most suitable model fitting the cell volume histograms. Several trial functions were tested and their parameters were tabulated. Healthy people exhibited an average of the cell volume of 85 femto liters while patients had 95 femto liters. White blood cell presented a small variation and platelets preserved their average for both populations.
The Effects of Statistical Analysis Software and Calculators on Statistics Achievement
ERIC Educational Resources Information Center
Christmann, Edwin P.
2009-01-01
This study compared the effects of microcomputer-based statistical software and hand-held calculators on the statistics achievement of university males and females. The subjects, 73 graduate students enrolled in univariate statistics classes at a public comprehensive university, were randomly assigned to groups that used either microcomputer-based…
Analysis and Evaluation of Supersonic Underwing Heat Addition
NASA Technical Reports Server (NTRS)
Luidens, Roger W.; Flaherty, Richard J.
1959-01-01
The linearized theory for heat addition under a wing has been developed to optimize wing geometry, heat addition, and angle of attack. The optimum wing has all of the thickness on the underside of the airfoil, with maximum-thickness point well downstream, has a moderate thickness ratio, and operates at an optimum angle of attack. The heat addition is confined between the fore Mach waves from under the trailing surface of the wing. By linearized theory, a wing at optimum angle of attack may have a range efficiency about twice that of a wing at zero angle of attack. More rigorous calculations using the method of characteristics for particular flow models were made for heating under a flat-plate wing and for several wings with thickness, both with heat additions concentrated near the wing. The more rigorous calculations yield in practical cases efficiencies about half those estimated by linear theory. An analysis indicates that distributing the heat addition between the fore waves from the undertrailing portion of the wing is a way of improving the performance, and further calculations appear desirable. A comparison of the conventional ramjet-plus wing with underwing heat addition when the heat addition is concentrated near the wing shows the ramjet to be superior on a range basis up to Mach number of about B. The heat distribution under the wing and the assumed ramjet and airframe performance may have a marked effect on this conclusion. Underwing heat addition can be useful in providing high-altitude maneuver capability at high flight Mach numbers for an airplane powered by conventional ramjets during cruise.
STATISTICAL ANALYSIS OF TANK 18F FLOOR SAMPLE RESULTS
Harris, S.
2010-09-02
Representative sampling has been completed for characterization of the residual material on the floor of Tank 18F as per the statistical sampling plan developed by Shine [1]. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL [2]. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples results [3] to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL{sub 95%}) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 18F. The uncertainty is quantified in this report by an upper 95% confidence limit (UCL{sub 95%}) on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL{sub 95%} was based entirely on the six current scrape sample results (each averaged across three analytical determinations).
STATISTICAL ANALYSIS OF TANK 19F FLOOR SAMPLE RESULTS
Harris, S.
2010-09-02
Representative sampling has been completed for characterization of the residual material on the floor of Tank 19F as per the statistical sampling plan developed by Harris and Shine. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples results to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL95%) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current scrape sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 19F. The uncertainty is quantified in this report by an UCL95% on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL95% was based entirely on the six current scrape sample results (each averaged across three analytical determinations).
Error Analysis of Terrestrial Laser Scanning Data by Means of Spherical Statistics and 3D Graphs
Cuartero, Aurora; Armesto, Julia; Rodríguez, Pablo G.; Arias, Pedro
2010-01-01
This paper presents a complete analysis of the positional errors of terrestrial laser scanning (TLS) data based on spherical statistics and 3D graphs. Spherical statistics are preferred because of the 3D vectorial nature of the spatial error. Error vectors have three metric elements (one module and two angles) that were analyzed by spherical statistics. A study case has been presented and discussed in detail. Errors were calculating using 53 check points (CP) and CP coordinates were measured by a digitizer with submillimetre accuracy. The positional accuracy was analyzed by both the conventional method (modular errors analysis) and the proposed method (angular errors analysis) by 3D graphics and numerical spherical statistics. Two packages in R programming language were performed to obtain graphics automatically. The results indicated that the proposed method is advantageous as it offers a more complete analysis of the positional accuracy, such as angular error component, uniformity of the vector distribution, error isotropy, and error, in addition the modular error component by linear statistics. PMID:22163461
Statistical analysis of static shape control in space structures
NASA Technical Reports Server (NTRS)
Burdisso, Ricardo A.; Haftka, Raphael T.
1990-01-01
The article addresses the problem of efficient analysis of the statistics of initial and corrected shape distortions in space structures. Two approaches for improving efficiency are considered. One is an adjoint technique for calculating distortion shapes: the second is a modal expansion of distortion shapes in terms of pseudo-vibration modes. The two techniques are applied to the problem of optimizing actuator locations on a 55 m radiometer antenna. The adjoint analysis technique is used with a discrete-variable optimization method. The modal approximation technique is coupled with a standard conjugate-gradient continuous optimization method. The agreement between the two sets of results is good, validating both the approximate analysis and optimality of the results.
STATISTICS. The reusable holdout: Preserving validity in adaptive data analysis.
Dwork, Cynthia; Feldman, Vitaly; Hardt, Moritz; Pitassi, Toniann; Reingold, Omer; Roth, Aaron
2015-08-01
Misapplication of statistical data analysis is a common cause of spurious discoveries in scientific research. Existing approaches to ensuring the validity of inferences drawn from data assume a fixed procedure to be performed, selected before the data are examined. In common practice, however, data analysis is an intrinsically adaptive process, with new analyses generated on the basis of data exploration, as well as the results of previous analyses on the same data. We demonstrate a new approach for addressing the challenges of adaptivity based on insights from privacy-preserving data analysis. As an application, we show how to safely reuse a holdout data set many times to validate the results of adaptively chosen analyses. PMID:26250683
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.
Managing Performance Analysis with Dynamic Statistical Projection Pursuit
Vetter, J.S.; Reed, D.A.
2000-05-22
Computer systems and applications are growing more complex. Consequently, performance analysis has become more difficult due to the complex, transient interrelationships among runtime components. To diagnose these types of performance issues, developers must use detailed instrumentation to capture a large number of performance metrics. Unfortunately, this instrumentation may actually influence the performance analysis, leading the developer to an ambiguous conclusion. In this paper, we introduce a technique for focusing a performance analysis on interesting performance metrics. This technique, called dynamic statistical projection pursuit, identifies interesting performance metrics that the monitoring system should capture across some number of processors. By reducing the number of performance metrics, projection pursuit can limit the impact of instrumentation on the performance of the target system and can reduce the volume of performance data.
ANALYSIS OF MPC ACCESS REQUIREMENTS FOR ADDITION OF FILLER MATERIALS
W. Wallin
1996-09-03
This analysis is prepared by the Mined Geologic Disposal System (MGDS) Waste Package Development Department (WPDD) in response to a request received via a QAP-3-12 Design Input Data Request (Ref. 5.1) from WAST Design (formerly MRSMPC Design). The request is to provide: Specific MPC access requirements for the addition of filler materials at the MGDS (i.e., location and size of access required). The objective of this analysis is to provide a response to the foregoing request. The purpose of this analysis is to provide a documented record of the basis for the response. The response is stated in Section 8 herein. The response is based upon requirements from an MGDS perspective.
ERIC Educational Resources Information Center
Metz, Anneke M.
2008-01-01
There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly…
Gis-Based Spatial Statistical Analysis of College Graduates Employment
NASA Astrophysics Data System (ADS)
Tang, R.
2012-07-01
It is urgently necessary to be aware of the distribution and employment status of college graduates for proper allocation of human resources and overall arrangement of strategic industry. This study provides empirical evidence regarding the use of geocoding and spatial analysis in distribution and employment status of college graduates based on the data from 2004-2008 Wuhan Municipal Human Resources and Social Security Bureau, China. Spatio-temporal distribution of employment unit were analyzed with geocoding using ArcGIS software, and the stepwise multiple linear regression method via SPSS software was used to predict the employment and to identify spatially associated enterprise and professionals demand in the future. The results show that the enterprises in Wuhan east lake high and new technology development zone increased dramatically from 2004 to 2008, and tended to distributed southeastward. Furthermore, the models built by statistical analysis suggest that the specialty of graduates major in has an important impact on the number of the employment and the number of graduates engaging in pillar industries. In conclusion, the combination of GIS and statistical analysis which helps to simulate the spatial distribution of the employment status is a potential tool for human resource development research.
Detection and analysis of statistical differences in anatomical shape.
Golland, Polina; Grimson, W Eric L; Shenton, Martha E; Kikinis, Ron
2005-02-01
We present a computational framework for image-based analysis and interpretation of statistical differences in anatomical shape between populations. Applications of such analysis include understanding developmental and anatomical aspects of disorders when comparing patients versus normal controls, studying morphological changes caused by aging, or even differences in normal anatomy, for example, differences between genders. Once a quantitative description of organ shape is extracted from input images, the problem of identifying differences between the two groups can be reduced to one of the classical questions in machine learning of constructing a classifier function for assigning new examples to one of the two groups while making as few misclassifications as possible. The resulting classifier must be interpreted in terms of shape differences between the two groups back in the image domain. We demonstrate a novel approach to such interpretation that allows us to argue about the identified shape differences in anatomically meaningful terms of organ deformation. Given a classifier function in the feature space, we derive a deformation that corresponds to the differences between the two classes while ignoring shape variability within each class. Based on this approach, we present a system for statistical shape analysis using distance transforms for shape representation and the support vector machines learning algorithm for the optimal classifier estimation and demonstrate it on artificially generated data sets, as well as real medical studies. PMID:15581813
Statistical energy analysis of a geared rotor system
NASA Technical Reports Server (NTRS)
Lim, Teik C.; Singh, Rajendra
1990-01-01
The vibroacoustic response of a generic geared rotor system is analyzed on an order of magnitude basis utilizing an approximate statistical energy analysis method. This model includes a theoretical coupling loss factor for a generic bearing component, which properly accounts for the vibration transmission through rolling element bearing. A simplified model of a NASA test stand that assumes vibratory energy flow from the gear mesh source to the casing through shafts and bearings is given as an example. Effects of dissipation loss factor and gearbox radiation efficiency models are studied by comparing predictions with NASA test results.
Statistical energy analysis of complex structures, phase 2
NASA Technical Reports Server (NTRS)
Trudell, R. W.; Yano, L. I.
1980-01-01
A method for estimating the structural vibration properties of complex systems in high frequency environments was investigated. The structure analyzed was the Materials Experiment Assembly, (MEA), which is a portion of the OST-2A payload for the space transportation system. Statistical energy analysis (SEA) techniques were used to model the structure and predict the structural element response to acoustic excitation. A comparison of the intial response predictions and measured acoustic test data is presented. The conclusions indicate that: the SEA predicted the response of primary structure to acoustic excitation over a wide range of frequencies; and the contribution of mechanically induced random vibration to the total MEA is not significant.
Multi-scale statistical analysis of coronal solar activity
Gamborino, Diana; del-Castillo-Negrete, Diego; Martinell, Julio J.
2016-07-08
Multi-filter images from the solar corona are used to obtain temperature maps that are analyzed using techniques based on proper orthogonal decomposition (POD) in order to extract dynamical and structural information at various scales. Exploring active regions before and after a solar flare and comparing them with quiet regions, we show that the multi-scale behavior presents distinct statistical properties for each case that can be used to characterize the level of activity in a region. Information about the nature of heat transport is also to be extracted from the analysis.
Data collection, computation and statistical analysis in psychophysiological experiments.
Buzzi, R; Wespi, J; Zwimpfer, J
1982-01-01
The system was designed to allow simultaneous monitoring of eight bioelectrical signals together with the necessary event markers. The data inputs are pulse code modulated, recorded on magnetic tape, and then read into a minicomputer. The computer permits the determination of parameters for the following signals: electrocardiogram (ECG), respiration (RESP), skin conductance changes (SCC), electromyogram (EMG), plethysmogram (PLET), pulse transmission time (PTT), and electroencephalogram (EEG). These parameters are determined for time blocks of selectable duration and read into a mainframe computer for further statistical analysis. PMID:7183101
Skylab 2 ground winds data reduction and statistical analysis
NASA Technical Reports Server (NTRS)
1974-01-01
A ground winds test was conducted on the Skylab 2 spacecraft in a subsonic wind tunnel and the results were tape recorded for analysis. The data reduction system used to analyze the tapes for full scale, first and second mode bending moments, or acceleration plots versus dynamic pressure or wind velocity is explained. Portions of the Skylab 2 tape data were analyzed statistically in the form of power spectral densities, autocorrelations, and cross correlations to introduce a concept of using system response decay as a measure of linear system damping.
Multi-scale statistical analysis of coronal solar activity
NASA Astrophysics Data System (ADS)
Gamborino, Diana; del-Castillo-Negrete, Diego; Martinell, Julio J.
2016-07-01
Multi-filter images from the solar corona are used to obtain temperature maps that are analyzed using techniques based on proper orthogonal decomposition (POD) in order to extract dynamical and structural information at various scales. Exploring active regions before and after a solar flare and comparing them with quiet regions, we show that the multi-scale behavior presents distinct statistical properties for each case that can be used to characterize the level of activity in a region. Information about the nature of heat transport is also to be extracted from the analysis.
Statistical Analysis in Genetic Studies of Mental Illnesses
Zhang, Heping
2011-01-01
Identifying the risk factors for mental illnesses is of significant public health importance. Diagnosis, stigma associated with mental illnesses, comorbidity, and complex etiologies, among others, make it very challenging to study mental disorders. Genetic studies of mental illnesses date back at least a century ago, beginning with descriptive studies based on Mendelian laws of inheritance. A variety of study designs including twin studies, family studies, linkage analysis, and more recently, genomewide association studies have been employed to study the genetics of mental illnesses, or complex diseases in general. In this paper, I will present the challenges and methods from a statistical perspective and focus on genetic association studies. PMID:21909187
Statistical Analysis of Strength Data for an Aerospace Aluminum Alloy
NASA Technical Reports Server (NTRS)
Neergaard, Lynn; Malone, Tina; Gentz, Steven J. (Technical Monitor)
2000-01-01
Aerospace vehicles are produced in limited quantities that do not always allow development of MIL-HDBK-5 A-basis design allowables. One method of examining production and composition variations is to perform 100% lot acceptance testing for aerospace Aluminum (Al) alloys. This paper discusses statistical trends seen in strength data for one Al alloy. A four-step approach reduced the data to residuals, visualized residuals as a function of time, grouped data with quantified scatter, and conducted analysis of variance (ANOVA).
Statistical Analysis of Strength Data for an Aerospace Aluminum Alloy
NASA Technical Reports Server (NTRS)
Neergaard, L.; Malone, T.
2001-01-01
Aerospace vehicles are produced in limited quantities that do not always allow development of MIL-HDBK-5 A-basis design allowables. One method of examining production and composition variations is to perform 100% lot acceptance testing for aerospace Aluminum (Al) alloys. This paper discusses statistical trends seen in strength data for one Al alloy. A four-step approach reduced the data to residuals, visualized residuals as a function of time, grouped data with quantified scatter, and conducted analysis of variance (ANOVA).
Spectral Envelopes and Additive + Residual Analysis/Synthesis
NASA Astrophysics Data System (ADS)
Rodet, Xavier; Schwarz, Diemo
The subject of this chapter is the estimation, representation, modification, and use of spectral envelopes in the context of sinusoidal-additive-plus-residual analysis/synthesis. A spectral envelope is an amplitude-vs-frequency function, which may be obtained from the envelope of a short-time spectrum (Rodet et al., 1987; Schwarz, 1998). [Precise definitions of such an envelope and short-time spectrum (STS) are given in Section 2.] The additive-plus-residual analysis/synthesis method is based on a representation of signals in terms of a sum of time-varying sinusoids and of a non-sinusoidal residual signal [e.g., see Serra (1989), Laroche et al. (1993), McAulay and Quatieri (1995), and Ding and Qian (1997)]. Many musical sound signals may be described as a combination of a nearly periodic waveform and colored noise. The nearly periodic part of the signal can be viewed as a sum of sinusoidal components, called partials, with time-varying frequency and amplitude. Such sinusoidal components are easily observed on a spectral analysis display (Fig. 5.1) as obtained, for instance, from a discrete Fourier transform.
Statistical analysis of the 70 meter antenna surface distortions
NASA Technical Reports Server (NTRS)
Kiedron, K.; Chian, C. T.; Chuang, K. L.
1987-01-01
Statistical analysis of surface distortions of the 70 meter NASA/JPL antenna, located at Goldstone, was performed. The purpose of this analysis is to verify whether deviations due to gravity loading can be treated as quasi-random variables with normal distribution. Histograms of the RF pathlength error distribution for several antenna elevation positions were generated. The results indicate that the deviations from the ideal antenna surface are not normally distributed. The observed density distribution for all antenna elevation angles is taller and narrower than the normal density, which results in large positive values of kurtosis and a significant amount of skewness. The skewness of the distribution changes from positive to negative as the antenna elevation changes from zenith to horizon.
Statistical analysis of cascading failures in power grids
Chertkov, Michael; Pfitzner, Rene; Turitsyn, Konstantin
2010-12-01
We introduce a new microscopic model of cascading failures in transmission power grids. This model accounts for automatic response of the grid to load fluctuations that take place on the scale of minutes, when optimum power flow adjustments and load shedding controls are unavailable. We describe extreme events, caused by load fluctuations, which cause cascading failures of loads, generators and lines. Our model is quasi-static in the causal, discrete time and sequential resolution of individual failures. The model, in its simplest realization based on the Directed Current description of the power flow problem, is tested on three standard IEEE systems consisting of 30, 39 and 118 buses. Our statistical analysis suggests a straightforward classification of cascading and islanding phases in terms of the ratios between average number of removed loads, generators and links. The analysis also demonstrates sensitivity to variations in line capacities. Future research challenges in modeling and control of cascading outages over real-world power networks are discussed.
Processes and subdivisions in diogenites, a multivariate statistical analysis
NASA Technical Reports Server (NTRS)
Harriott, T. A.; Hewins, R. H.
1984-01-01
Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.
Vibroacoustic optimization using a statistical energy analysis model
NASA Astrophysics Data System (ADS)
Culla, Antonio; D`Ambrogio, Walter; Fregolent, Annalisa; Milana, Silvia
2016-08-01
In this paper, an optimization technique for medium-high frequency dynamic problems based on Statistical Energy Analysis (SEA) method is presented. Using a SEA model, the subsystem energies are controlled by internal loss factors (ILF) and coupling loss factors (CLF), which in turn depend on the physical parameters of the subsystems. A preliminary sensitivity analysis of subsystem energy to CLF's is performed to select CLF's that are most effective on subsystem energies. Since the injected power depends not only on the external loads but on the physical parameters of the subsystems as well, it must be taken into account under certain conditions. This is accomplished in the optimization procedure, where approximate relationships between CLF's, injected power and physical parameters are derived. The approach is applied on a typical aeronautical structure: the cabin of a helicopter.
First statistical analysis of Geant4 quality software metrics
NASA Astrophysics Data System (ADS)
Ronchieri, Elisabetta; Grazia Pia, Maria; Giacomini, Francesco
2015-12-01
Geant4 is a simulation system of particle transport through matter, widely used in several experimental areas from high energy physics and nuclear experiments to medical studies. Some of its applications may involve critical use cases; therefore they would benefit from an objective assessment of the software quality of Geant4. In this paper, we provide a first statistical evaluation of software metrics data related to a set of Geant4 physics packages. The analysis aims at identifying risks for Geant4 maintainability, which would benefit from being addressed at an early stage. The findings of this pilot study set the grounds for further extensions of the analysis to the whole of Geant4 and to other high energy physics software systems.
Detection of bearing damage by statistic vibration analysis
NASA Astrophysics Data System (ADS)
Sikora, E. A.
2016-04-01
The condition of bearings, which are essential components in mechanisms, is crucial to safety. The analysis of the bearing vibration signal, which is always contaminated by certain types of noise, is a very important standard for mechanical condition diagnosis of the bearing and mechanical failure phenomenon. In this paper the method of rolling bearing fault detection by statistical analysis of vibration is proposed to filter out Gaussian noise contained in a raw vibration signal. The results of experiments show that the vibration signal can be significantly enhanced by application of the proposed method. Besides, the proposed method is used to analyse real acoustic signals of a bearing with inner race and outer race faults, respectively. The values of attributes are determined according to the degree of the fault. The results confirm that the periods between the transients, which represent bearing fault characteristics, can be successfully detected.
FRATS: Functional Regression Analysis of DTI Tract Statistics
Zhu, Hongtu; Styner, Martin; Tang, Niansheng; Liu, Zhexing; Lin, Weili; Gilmore, John H.
2010-01-01
Diffusion tensor imaging (DTI) provides important information on the structure of white matter fiber bundles as well as detailed tissue properties along these fiber bundles in vivo. This paper presents a functional regression framework, called FRATS, for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The functional regression framework consists of four integrated components: the local polynomial kernel method for smoothing multiple diffusion properties along individual fiber bundles, a functional linear model for characterizing the association between fiber bundle diffusion properties and a set of covariates, a global test statistic for testing hypotheses of interest, and a resampling method for approximating the p-value of the global test statistic. The proposed methodology is applied to characterizing the development of five diffusion properties including fractional anisotropy, mean diffusivity, and the three eigenvalues of diffusion tensor along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. Significant age and gestational age effects on the five diffusion properties were found in both tracts. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. PMID:20335089
Statistical analysis and correlation discovery of tumor respiratory motion.
Wu, Huanmei; Sharp, Gregory C; Zhao, Qingya; Shirato, Hiroki; Jiang, Steve B
2007-08-21
Tumors, especially in the thorax and abdomen, are subject to respiratory motion, and understanding the structure of respiratory motion is a key component to the management and control of disease in these sites. We have applied statistical analysis and correlation discovery methods to analyze and mine tumor respiratory motion based on a finite state model of tumor motion. Aggregates (such as minimum, maximum, average and mean), histograms, percentages, linear regression and multi-round statistical analysis have been explored. The results have been represented in various formats, including tables, graphs and text description. Different graphs, for example scatter plots, clustered column figures, 100% stacked column figures and box-whisker plots, have been applied to highlight different aspects of the results. The internal tumor motion from 42 lung tumors, 30 of which have motion larger than 5 mm, has been analyzed. Results for both inter-patient and intra-patient motion characteristics, such as duration and travel distance patterns, are reported. New knowledge of patient-specific tumor motion characteristics have been discovered, such as expected correlations between properties. The discovered tumor motion characteristics will be utilized in different aspects of image-guided radiation treatment, including treatment planning, online tumor motion prediction and real-time radiation dose delivery. PMID:17671334
Statistical analysis and correlation discovery of tumor respiratory motion
NASA Astrophysics Data System (ADS)
Wu, Huanmei; Sharp, Gregory C.; Zhao, Qingya; Shirato, Hiroki; Jiang, Steve B.
2007-08-01
Tumors, especially in the thorax and abdomen, are subject to respiratory motion, and understanding the structure of respiratory motion is a key component to the management and control of disease in these sites. We have applied statistical analysis and correlation discovery methods to analyze and mine tumor respiratory motion based on a finite state model of tumor motion. Aggregates (such as minimum, maximum, average and mean), histograms, percentages, linear regression and multi-round statistical analysis have been explored. The results have been represented in various formats, including tables, graphs and text description. Different graphs, for example scatter plots, clustered column figures, 100% stacked column figures and box-whisker plots, have been applied to highlight different aspects of the results. The internal tumor motion from 42 lung tumors, 30 of which have motion larger than 5 mm, has been analyzed. Results for both inter-patient and intra-patient motion characteristics, such as duration and travel distance patterns, are reported. New knowledge of patient-specific tumor motion characteristics have been discovered, such as expected correlations between properties. The discovered tumor motion characteristics will be utilized in different aspects of image-guided radiation treatment, including treatment planning, online tumor motion prediction and real-time radiation dose delivery.
EBprot: Statistical analysis of labeling-based quantitative proteomics data.
Koh, Hiromi W L; Swa, Hannah L F; Fermin, Damian; Ler, Siok Ghee; Gunaratne, Jayantha; Choi, Hyungwon
2015-08-01
Labeling-based proteomics is a powerful method for detection of differentially expressed proteins (DEPs). The current data analysis platform typically relies on protein-level ratios, which is obtained by summarizing peptide-level ratios for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is not incorporated into the differential expression (DE) analysis. Here, we propose a novel probabilistic framework EBprot that directly models the peptide-protein hierarchy and rewards the proteins with reproducible evidence of DE over multiple peptides. To evaluate its performance with known DE states, we conducted a simulation study to show that the peptide-level analysis of EBprot provides better receiver-operating characteristic and more accurate estimation of the false discovery rates than the methods based on protein-level ratios. We also demonstrate superior classification performance of peptide-level EBprot analysis in a spike-in dataset. To illustrate the wide applicability of EBprot in different experimental designs, we applied EBprot to a dataset for lung cancer subtype analysis with biological replicates and another dataset for time course phosphoproteome analysis of EGF-stimulated HeLa cells with multiplexed labeling. Through these examples, we show that the peptide-level analysis of EBprot is a robust alternative to the existing statistical methods for the DE analysis of labeling-based quantitative datasets. The software suite is freely available on the Sourceforge website http://ebprot.sourceforge.net/. All MS data have been deposited in the ProteomeXchange with identifier PXD001426 (http://proteomecentral.proteomexchange.org/dataset/PXD001426/). PMID:25913743
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 analysis of magnetotail fast flows and related magnetic disturbances
NASA Astrophysics Data System (ADS)
Frühauff, Dennis; Glassmeier, Karl-Heinz
2016-04-01
This study presents an investigation on the occurrence of fast flows in the magnetotail using the complete available data set of the THEMIS spacecraft for the years 2007 to 2015. The fast flow events (times of enhanced ion velocity) are detected through the use of a velocity criterion, therefore making the resulting database as large as almost 16 000 events. First, basic statistical findings concerning velocity distributions, occurrence rates, group structures are presented. Second, Superposed Epoch Analysis is utilized to account for average profiles of selected plasma quantities. The data reveal representative time series in near and far tail of the Earth with typical timescales of the order of 1-2 min, corresponding to scale sizes of 3 RE. Last, related magnetic field disturbances are analyzed. It is found that the minimum variance direction is essentially confined to a plane almost perpendicular to the main flow direction while, at the same time, the maximum variance direction is aligned with flow and background field directions. The presentation of the database and first statistical findings will prove useful both as input for magneto-hydrodynamical simulations and theoretical considerations of fast flows.
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).
ADS-Demo Fuel Rod Performance: Multivariate Statistical Analysis
Calabrese, R.; Vettraino, F.; Luzzi, L.
2004-07-01
A forward step in the development of Accelerator Driven System (ADS) for the Pu, MA and LLFP transmutation, is the realisation of a 80 MWt ADS-demo (XADS) whose basic objective is the system feasibility demonstration. The XADS is forecasted to adopt the UO{sub 2}-PuO{sub 2} mixed-oxides fuel already experimented in the sodium cooled fast reactors such as the french SPX-1. The present multivariate statistical analysis performed by using the Transuranus Code, was carried out for the Normal Operation at the so-called Enhanced Nominal Conditions (120% nominal reactor power), aimed at verifying that the fuel system complies with the stated design limits, i.e. centerline fuel temperature, cladding temperature and damage, during all the in-reactor lifetime. A statistical input set similar to SPX and PEC fuel case, was adopted. One most relevant assumption in the present calculations was a 30% AISI-316 cladding thickness corrosion at EOL. Relative influence of main fuel rod parameters on fuel centerline temperature was also evaluated. (authors)
Statistical analysis of target acquisition sensor modeling experiments
NASA Astrophysics Data System (ADS)
Deaver, Dawne M.; Moyer, Steve
2015-05-01
The U.S. Army RDECOM CERDEC NVESD Modeling and Simulation Division is charged with the development and advancement of military target acquisition models to estimate expected soldier performance when using all types of imaging sensors. Two elements of sensor modeling are (1) laboratory-based psychophysical experiments used to measure task performance and calibrate the various models and (2) field-based experiments used to verify the model estimates for specific sensors. In both types of experiments, it is common practice to control or measure environmental, sensor, and target physical parameters in order to minimize uncertainty of the physics based modeling. Predicting the minimum number of test subjects required to calibrate or validate the model should be, but is not always, done during test planning. The objective of this analysis is to develop guidelines for test planners which recommend the number and types of test samples required to yield a statistically significant result.
Treated cabin acoustic prediction using statistical energy analysis
NASA Technical Reports Server (NTRS)
Yoerkie, Charles A.; Ingraham, Steven T.; Moore, James A.
1987-01-01
The application of statistical energy analysis (SEA) to the modeling and design of helicopter cabin interior noise control treatment is demonstrated. The information presented here is obtained from work sponsored at NASA Langley for the development of analytic modeling techniques and the basic understanding of cabin noise. Utility and executive interior models are developed directly from existing S-76 aircraft designs. The relative importance of panel transmission loss (TL), acoustic leakage, and absorption to the control of cabin noise is shown using the SEA modeling parameters. It is shown that the major cabin noise improvement below 1000 Hz comes from increased panel TL, while above 1000 Hz it comes from reduced acoustic leakage and increased absorption in the cabin and overhead cavities.
Statistical approach to the analysis of cell desynchronization data
NASA Astrophysics Data System (ADS)
Milotti, Edoardo; Del Fabbro, Alessio; Dalla Pellegrina, Chiara; Chignola, Roberto
2008-07-01
Experimental measurements on semi-synchronous tumor cell populations show that after a few cell cycles they desynchronize completely, and this desynchronization reflects the intercell variability of cell-cycle duration. It is important to identify the sources of randomness that desynchronize a population of cells living in a homogeneous environment: for example, being able to reduce randomness and induce synchronization would aid in targeting tumor cells with chemotherapy or radiotherapy. Here we describe a statistical approach to the analysis of the desynchronization measurements that is based on minimal modeling hypotheses, and can be derived from simple heuristics. We use the method to analyze existing desynchronization data and to draw conclusions on the randomness of cell growth and proliferation.
Barcode localization with region based gradient statistical analysis
NASA Astrophysics Data System (ADS)
Chen, Zhiyuan; Zhao, Yuming
2015-03-01
Barcode, as a kind of data representation method, has been adopted in a wide range of areas. Especially with the rise of the smart phone and the hand-held device equipped with high resolution camera and great computation power, barcode technique has found itself more extensive applications. In industrial field, barcode reading system is highly demanded to be robust to blur, illumination change, pitch, rotation, and scale change. This paper gives a new idea in localizing barcode under a region-based gradient statistical analysis. Making this idea as the basis, four algorithms have been developed for dealing with Linear, PDF417, Stacked 1D1D and Stacked 1D2D barcodes respectively. After being evaluated on our challenging dataset with more than 17000 images, the result shows that our methods can achieve an average localization accuracy of 82.17% with respect to 8 kinds of distortions and within an average time of 12 ms.
Statistical analysis of test data for APM rod issue
Edwards, T.B.; Harris, S.P.; Reeve, C.P.
1992-05-01
The uncertainty associated with the use of the K-Reactor axial power monitors (APMs) to measure roof-top-ratios is investigated in this report. Internal heating test data acquired under both DC-flow conditions and AC-flow conditions have been analyzed. These tests were conducted to simulate gamma heating at the lower power levels planned for reactor operation. The objective of this statistical analysis is to investigate the relationship between the observed and true roof-top-ratio (RTR) values and associated uncertainties at power levels within this lower operational range. Conditional on a given, known power level, a prediction interval for the true RTR value corresponding to a new, observed RTR is given. This is done for a range of power levels. Estimates of total system uncertainty are also determined by combining the analog-to-digital converter uncertainty with the results from the test data.
The geomagnetic storms of 2015: Statistical analysis and forecasting results
NASA Astrophysics Data System (ADS)
Paouris, Evangelos; Gerontidou, Maria; Mavromichalaki, Helen
2016-04-01
The year 2015 was characterized by long geomagnetic quiet periods with a lot of geomagnetically active breaks although it is on the declining phase of the current solar cycle. As a result a number of geomagnetic storms in the G1 up to G4 scale were noticed. In this work the characteristics of these geomagnetic storms like the scale level, the origin of the storm (CME or CIR) and the duration have been studied. Furthermore, a statistical analysis of these events and a comparative study of the forecasting and the actual geomagnetic conditions are performed using data from the NOAA space weather forecasting center and from the Athens Space Weather Forecasting Center as well. These forecasting centers estimate and provide every day the geomagnetic conditions for the upcoming days giving the values of the geomagnetic index Ap. The forecasting values of Ap index for the year 2015 from these two centers and their comparison in terms of the actual values are discussed.
Dynamic Modelling and Statistical Analysis of Event Times
Peña, Edsel A.
2006-01-01
This review article provides an overview of recent work in the modelling and analysis of recurrent events arising in engineering, reliability, public health, biomedical, and other areas. Recurrent event modelling possesses unique facets making it different and more difficult to handle than single event settings. For instance, the impact of an increasing number of event occurrences needs to be taken into account, the effects of covariates should be considered, potential association among the inter-event times within a unit cannot be ignored, and the effects of performed interventions after each event occurrence need to be factored in. A recent general class of models for recurrent events which simultaneously accommodates these aspects is described. Statistical inference methods for this class of models are presented and illustrated through applications to real data sets. Some existing open research problems are described. PMID:17906740
Statistical analysis of honeybee survival after chronic exposure to insecticides.
Dechaume Moncharmont, François-Xavier; Decourtye, Axel; Hennequet-Hantier, Christelle; Pons, Odile; Pham-Delègue, Minh-Hà
2003-12-01
Studies concerning long-term survival of honeybees raise the problem of the statistical analysis of mortality data. In the present study, we used a modeling approach of survival data of caged bees under chronic exposure to two pesticides (imidacloprid and deltamethrin). Our model, based on a Cox proportional hazard model, is not restricted to a specific hazard functional form, such as in parametric approaches, but takes into account multiple covariates. We consider not only the pesticide treatment but also a nuisance variable (variability between replicates). Moreover, considering the occurrence of social interactions, the model integrates the fact that bees do not die independently of each other. We demonstrate the chronic toxicity induced by imidacloprid and deltamethrin. Our results also underline the role of the replicate effect, the density-dependent effect, and their interactions with the treatment effect. None of these parameters can be neglected in the assessment of chronic toxicity of pesticides to the honeybee. PMID:14713054
Statistical analysis of the uncertainty related to flood hazard appraisal
NASA Astrophysics Data System (ADS)
Notaro, Vincenza; Freni, Gabriele
2015-12-01
The estimation of flood hazard frequency statistics for an urban catchment is of great interest in practice. It provides the evaluation of potential flood risk and related damage and supports decision making for flood risk management. Flood risk is usually defined as function of the probability, that a system deficiency can cause flooding (hazard), and the expected damage, due to the flooding magnitude (damage), taking into account both the exposure and the vulnerability of the goods at risk. The expected flood damage can be evaluated by an a priori estimation of potential damage caused by flooding or by interpolating real damage data. With regard to flood hazard appraisal several procedures propose to identify some hazard indicator (HI) such as flood depth or the combination of flood depth and velocity and to assess the flood hazard corresponding to the analyzed area comparing the HI variables with user-defined threshold values or curves (penalty curves or matrixes). However, flooding data are usually unavailable or piecemeal allowing for carrying out a reliable flood hazard analysis, therefore hazard analysis is often performed by means of mathematical simulations aimed at evaluating water levels and flow velocities over catchment surface. As results a great part of the uncertainties intrinsic to flood risk appraisal can be related to the hazard evaluation due to the uncertainty inherent to modeling results and to the subjectivity of the user defined hazard thresholds applied to link flood depth to a hazard level. In the present work, a statistical methodology was proposed for evaluating and reducing the uncertainties connected with hazard level estimation. The methodology has been applied to a real urban watershed as case study.
Helioseismology of pre-emerging active regions. III. Statistical analysis
Barnes, G.; Leka, K. D.; Braun, D. C.; Birch, A. C.
2014-05-01
The subsurface properties of active regions (ARs) prior to their appearance at the solar surface may shed light on the process of AR formation. Helioseismic holography has been applied to samples taken from two populations of regions on the Sun (pre-emergence and without emergence), each sample having over 100 members, that were selected to minimize systematic bias, as described in Paper I. Paper II showed that there are statistically significant signatures in the average helioseismic properties that precede the formation of an AR. This paper describes a more detailed analysis of the samples of pre-emergence regions and regions without emergence based on discriminant analysis. The property that is best able to distinguish the populations is found to be the surface magnetic field, even a day before the emergence time. However, after accounting for the correlations between the surface field and the quantities derived from helioseismology, there is still evidence of a helioseismic precursor to AR emergence that is present for at least a day prior to emergence, although the analysis presented cannot definitively determine the subsurface properties prior to emergence due to the small sample sizes.
Statistical analysis of effects of measures against agricultural pollution.
Sæbø, H V
1991-01-01
The Norwegian Government has initiated a plan to reduce agricultural pollution. One of the projects in this plan is aimed at investigating the effects of different measures in order to evaluate their effects and costs.A set of experiments has been designed to estimate the effects of measures to reduce or control the use of fertilizers and erosion. The project started in 1985. It comprises continuous measurements in two water courses in each of four counties: one test drainage area where the relevant measures were implemented at the end of 1986, and one reference area where no specific measures are carried out. A series of chemical parameters are measured together with runoff and other hydrological and meteorogical data.The paper provides a preliminary analysis of the data collected in one of the counties during the period June 1985 to April 1988. It contains examples of analysis of covariance to show possible effects of the measures carried out in the test area.Natural variations in precipitation and pollution are large, making it difficult to see the effects of the measures without using statistical techniques to take the multivariability of the problem into account. Some effects can be shown with analysis of covariance. However, the relatively short measurement period makes it neccessary to be careful when interpreting the results. PMID:24233499
Classification of Malaysia aromatic rice using multivariate statistical analysis
Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.
2015-05-15
Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.
Classification of Malaysia aromatic rice using multivariate statistical analysis
NASA Astrophysics Data System (ADS)
Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.
2015-05-01
Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.
Zhu, Xiaofeng; Feng, Tao; Tayo, Bamidele O.; Liang, Jingjing; Young, J. Hunter; Franceschini, Nora; Smith, Jennifer A.; Yanek, Lisa R.; Sun, Yan V.; Edwards, Todd L.; Chen, Wei; Nalls, Mike; Fox, Ervin; Sale, Michele; Bottinger, Erwin; Rotimi, Charles; Liu, Yongmei; McKnight, Barbara; Liu, Kiang; Arnett, Donna K.; Chakravati, Aravinda; Cooper, Richard S.; Redline, Susan
2015-01-01
Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple—even distinct—traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10−8) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10−7) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes. PMID:25500260
Zhu, Xiaofeng; Feng, Tao; Tayo, Bamidele O; Liang, Jingjing; Young, J Hunter; Franceschini, Nora; Smith, Jennifer A; Yanek, Lisa R; Sun, Yan V; Edwards, Todd L; Chen, Wei; Nalls, Mike; Fox, Ervin; Sale, Michele; Bottinger, Erwin; Rotimi, Charles; Liu, Yongmei; McKnight, Barbara; Liu, Kiang; Arnett, Donna K; Chakravati, Aravinda; Cooper, Richard S; Redline, Susan
2015-01-01
Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes. PMID:25500260