Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

The Design and Analysis of Transposon-Insertion Sequencing Experiments

PubMed Central

Chao, Michael C.; Abel, Sören; Davis, Brigid M.; Waldor, Matthew K.

2016-01-01

Preface Transposon-insertion sequencing (TIS) is a powerful approach that can be widely applied to genome-wide definition of loci that are required for growth in diverse conditions. However, experimental design choices and stochastic biological processes can heavily influence the results of TIS experiments and affect downstream statistical analysis. Here, we discuss TIS experimental parameters and how these factors relate to the benefits and limitations of the various statistical frameworks that can be applied to computational analysis of TIS data. PMID:26775926

Research Design and Statistics for Applied Linguistics.

ERIC Educational Resources Information Center

Hatch, Evelyn; Farhady, Hossein

An introduction to the conventions of research design and statistical analysis is presented for graduate students of applied linguistics. The chapters cover such concepts as the definition of research, variables, research designs, research report formats, sorting and displaying data, probability and hypothesis testing, comparing means,…

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

The estimation of the measurement results with using statistical methods

NASA Astrophysics Data System (ADS)

Velychko, O.; Gordiyenko, T.

2015-02-01

The row of international standards and guides describe various statistical methods that apply for a management, control and improvement of processes with the purpose of realization of analysis of the technical measurement results. The analysis of international standards and guides on statistical methods estimation of the measurement results recommendations for those applications in laboratories is described. For realization of analysis of standards and guides the cause-and-effect Ishikawa diagrams concerting to application of statistical methods for estimation of the measurement results are constructed.

A Meta-analysis of Gender Differences in Applied Statistics Achievement.

ERIC Educational Resources Information Center

Schram, Christine M.

1996-01-01

A meta-analysis of gender differences examined statistics achievement in postsecondary level psychology, education, and business courses. Analysis of 13 articles (18 samples) found that undergraduate males had an advantage, outscoring females when the outcome was a series of examinations. Females outscored males when the outcome was total course…

Research of Extension of the Life Cycle of Helicopter Rotor Blade in Hungary

DTIC Science & Technology

2003-02-01

Radiography (DXR), and (iii) Vibration Diagnostics (VD) with Statistical Energy Analysis (SEA) were semi- simultaneously applied [1]. The used three...2.2. Vibration Diagnostics (VD)) Parallel to the NDT measurements the Statistical Energy Analysis (SEA) as a vibration diagnostical tool were...noises were analysed with a dual-channel real time frequency analyser (BK2035). In addition to the Statistical Energy Analysis measurement a small

A crash course on data analysis in asteroseismology

NASA Astrophysics Data System (ADS)

Appourchaux, Thierry

2014-02-01

In this course, I try to provide a few basics required for performing data analysis in asteroseismology. First, I address how one can properly treat times series: the sampling, the filtering effect, the use of Fourier transform, the associated statistics. Second, I address how one can apply statistics for decision making and for parameter estimation either in a frequentist of a Bayesian framework. Last, I review how these basic principle have been applied (or not) in asteroseismology.

Study designs, use of statistical tests, and statistical analysis software choice in 2015: Results from two Pakistani monthly Medline indexed journals.

PubMed

Shaikh, Masood Ali

2017-09-01

Assessment of research articles in terms of study designs used, statistical tests applied and the use of statistical analysis programmes help determine research activity profile and trends in the country. In this descriptive study, all original articles published by Journal of Pakistan Medical Association (JPMA) and Journal of the College of Physicians and Surgeons Pakistan (JCPSP), in the year 2015 were reviewed in terms of study designs used, application of statistical tests, and the use of statistical analysis programmes. JPMA and JCPSP published 192 and 128 original articles, respectively, in the year 2015. Results of this study indicate that cross-sectional study design, bivariate inferential statistical analysis entailing comparison between two variables/groups, and use of statistical software programme SPSS to be the most common study design, inferential statistical analysis, and statistical analysis software programmes, respectively. These results echo previously published assessment of these two journals for the year 2014.

Applying Statistics in the Undergraduate Chemistry Laboratory: Experiments with Food Dyes.

ERIC Educational Resources Information Center

Thomasson, Kathryn; Lofthus-Merschman, Sheila; Humbert, Michelle; Kulevsky, Norman

1998-01-01

Describes several experiments to teach different aspects of the statistical analysis of data using household substances and a simple analysis technique. Each experiment can be performed in three hours. Students learn about treatment of spurious data, application of a pooled variance, linear least-squares fitting, and simultaneous analysis of dyes…

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.

For the Love of Statistics: Appreciating and Learning to Apply Experimental Analysis and Statistics through Computer Programming Activities

ERIC Educational Resources Information Center

Mascaró, Maite; Sacristán, Ana Isabel; Rufino, Marta M.

2016-01-01

For the past 4 years, we have been involved in a project that aims to enhance the teaching and learning of experimental analysis and statistics, of environmental and biological sciences students, through computational programming activities (using R code). In this project, through an iterative design, we have developed sequences of R-code-based…

Multivariate analysis, mass balance techniques, and statistical tests as tools in igneous petrology: application to the Sierra de las Cruces volcanic range (Mexican Volcanic Belt).

PubMed

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures).

Two Paradoxes in Linear Regression Analysis.

PubMed

Feng, Ge; Peng, Jing; Tu, Dongke; Zheng, Julia Z; Feng, Changyong

2016-12-25

Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection.

A Study on Predictive Analytics Application to Ship Machinery Maintenance

DTIC Science & Technology

2013-09-01

Looking at the nature of the time series forecasting method , it would be better applied to offline analysis . The application for real- time online...other system attributes in future. Two techniques of statistical analysis , mainly time series models and cumulative sum control charts, are discussed in...statistical tool employed for the two techniques of statistical analysis . Both time series forecasting as well as CUSUM control charts are shown to be

A Survey of Probabilistic Methods for Dynamical Systems with Uncertain Parameters.

DTIC Science & Technology

1986-05-01

J., "An Approach to the Theoretical Background of Statistical Energy Analysis Applied to Structural Vibration," Journ. Acoust. Soc. Amer., Vol. 69...1973, Sect. 8.3. 80. Lyon, R.H., " Statistical Energy Analysis of Dynamical Systems," M.I.T. Press, 1975. e) Late References added in Proofreading !! 81...Dowell, E.H., and Kubota, Y., "Asymptotic Modal Analysis and ’~ y C-" -165- Statistical Energy Analysis of Dynamical Systems," Journ. Appi. - Mech

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.

Pathway analysis with next-generation sequencing data.

PubMed

Zhao, Jinying; Zhu, Yun; Boerwinkle, Eric; Xiong, Momiao

2015-04-01

Although pathway analysis methods have been developed and successfully applied to association studies of common variants, the statistical methods for pathway-based association analysis of rare variants have not been well developed. Many investigators observed highly inflated false-positive rates and low power in pathway-based tests of association of rare variants. The inflated false-positive rates and low true-positive rates of the current methods are mainly due to their lack of ability to account for gametic phase disequilibrium. To overcome these serious limitations, we develop a novel statistic that is based on the smoothed functional principal component analysis (SFPCA) for pathway association tests with next-generation sequencing data. The developed statistic has the ability to capture position-level variant information and account for gametic phase disequilibrium. By intensive simulations, we demonstrate that the SFPCA-based statistic for testing pathway association with either rare or common or both rare and common variants has the correct type 1 error rates. Also the power of the SFPCA-based statistic and 22 additional existing statistics are evaluated. We found that the SFPCA-based statistic has a much higher power than other existing statistics in all the scenarios considered. To further evaluate its performance, the SFPCA-based statistic is applied to pathway analysis of exome sequencing data in the early-onset myocardial infarction (EOMI) project. We identify three pathways significantly associated with EOMI after the Bonferroni correction. In addition, our preliminary results show that the SFPCA-based statistic has much smaller P-values to identify pathway association than other existing methods.

Statistical analysis of solid waste composition data: Arithmetic mean, standard deviation and correlation coefficients.

PubMed

Edjabou, Maklawe Essonanawe; Martín-Fernández, Josep Antoni; Scheutz, Charlotte; Astrup, Thomas Fruergaard

2017-11-01

Data for fractional solid waste composition provide relative magnitudes of individual waste fractions, the percentages of which always sum to 100, thereby connecting them intrinsically. Due to this sum constraint, waste composition data represent closed data, and their interpretation and analysis require statistical methods, other than classical statistics that are suitable only for non-constrained data such as absolute values. However, the closed characteristics of waste composition data are often ignored when analysed. The results of this study showed, for example, that unavoidable animal-derived food waste amounted to 2.21±3.12% with a confidence interval of (-4.03; 8.45), which highlights the problem of the biased negative proportions. A Pearson's correlation test, applied to waste fraction generation (kg mass), indicated a positive correlation between avoidable vegetable food waste and plastic packaging. However, correlation tests applied to waste fraction compositions (percentage values) showed a negative association in this regard, thus demonstrating that statistical analyses applied to compositional waste fraction data, without addressing the closed characteristics of these data, have the potential to generate spurious or misleading results. Therefore, ¨compositional data should be transformed adequately prior to any statistical analysis, such as computing mean, standard deviation and correlation coefficients. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improving Data Analysis in Second Language Acquisition by Utilizing Modern Developments in Applied Statistics

ERIC Educational Resources Information Center

Larson-Hall, Jenifer; Herrington, Richard

2010-01-01

In this article we introduce language acquisition researchers to two broad areas of applied statistics that can improve the way data are analyzed. First we argue that visual summaries of information are as vital as numerical ones, and suggest ways to improve them. Specifically, we recommend choosing boxplots over barplots and adding locally…

Consistent Tolerance Bounds for Statistical Distributions

NASA Technical Reports Server (NTRS)

Mezzacappa, M. A.

1983-01-01

Assumption that sample comes from population with particular distribution is made with confidence C if data lie between certain bounds. These "confidence bounds" depend on C and assumption about distribution of sampling errors around regression line. Graphical test criteria using tolerance bounds are applied in industry where statistical analysis influences product development and use. Applied to evaluate equipment life.

Two Paradoxes in Linear Regression Analysis

PubMed Central

FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong

2016-01-01

Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214

[Statistical analysis of articles in "Chinese journal of applied physiology" from 1999 to 2008].

PubMed

Du, Fei; Fang, Tao; Ge, Xue-ming; Jin, Peng; Zhang, Xiao-hong; Sun, Jin-li

2010-05-01

To evaluate the academic level and influence of "Chinese Journal of Applied Physiology" through statistical analysis for the fund sponsored articles published in the recent ten years. The articles of "Chinese Journal of Applied Physiology" from 1999 to 2008 were investigated. The number and the percentage of the fund sponsored articles, the fund organization and the author region were quantitatively analyzed by using the literature metrology method. The number of the fund sponsored articles increased unceasingly. The ratio of the fund from local government significantly enhanced in the latter five years. Most of the articles were from institutes located at Beijing, Zhejiang and Tianjin. "Chinese Journal of Applied Physiology" has a fine academic level and social influence.

Transforming Graph Data for Statistical Relational Learning

DTIC Science & Technology

2012-10-01

Jordan, 2003), PLSA (Hofmann, 1999), ? Classification via RMN (Taskar et al., 2003) or SVM (Hasan, Chaoji, Salem , & Zaki, 2006) ? Hierarchical...dimensionality reduction methods such as Principal 407 Rossi, McDowell, Aha, & Neville Component Analysis (PCA), Principal Factor Analysis ( PFA ), and...clustering algorithm. Journal of the Royal Statistical Society. Series C, Applied statistics, 28, 100–108. Hasan, M. A., Chaoji, V., Salem , S., & Zaki, M

The Use of a Context-Based Information Retrieval Technique

DTIC Science & Technology

2009-07-01

provided in context. Latent Semantic Analysis (LSA) is a statistical technique for inferring contextual and structural information, and previous studies...WAIS). 10 DSTO-TR-2322 1.4.4 Latent Semantic Analysis LSA, which is also known as latent semantic indexing (LSI), uses a statistical and...1.4.6 Language Models In contrast, natural language models apply algorithms that combine statistical information with semantic information. Semantic

Survival analysis, or what to do with upper limits in astronomical surveys

NASA Technical Reports Server (NTRS)

Isobe, Takashi; Feigelson, Eric D.

1986-01-01

A field of applied statistics called survival analysis has been developed over several decades to deal with censored data, which occur in astronomical surveys when objects are too faint to be detected. How these methods can assist in the statistical interpretation of astronomical data are reviewed.

Effects of Heterogeniety on Spatial Pattern Analysis of Wild Pistachio Trees in Zagros Woodlands, Iran

NASA Astrophysics Data System (ADS)

Erfanifard, Y.; Rezayan, F.

2014-10-01

Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation function g) is widely used in ecology to develop hypothesis on underlying processes by characterizing spatial patterns of vegetation. The aim of this study was to demonstrate effects of underlying heterogeneity of wild pistachio (Pistacia atlantica Desf.) trees on the second-order summary statistics of point pattern analysis in a part of Zagros woodlands, Iran. The spatial distribution of 431 wild pistachio trees was accurately mapped in a 40 ha stand in the Wild Pistachio & Almond Research Site, Fars province, Iran. Three commonly used second-order summary statistics (i.e., K-, L-, and g-functions) were applied to analyse their spatial pattern. The two-sample Kolmogorov-Smirnov goodness-of-fit test showed that the observed pattern significantly followed an inhomogeneous Poisson process null model in the study region. The results also showed that heterogeneous pattern of wild pistachio trees biased the homogeneous form of K-, L-, and g-functions, demonstrating a stronger aggregation of the trees at the scales of 0-50 m than actually existed and an aggregation at scales of 150-200 m, while regularly distributed. Consequently, we showed that heterogeneity of point patterns may bias the results of homogeneous second-order summary statistics and we also suggested applying inhomogeneous summary statistics with related null models for spatial pattern analysis of heterogeneous vegetations.

FUNSTAT and statistical image representations

NASA Technical Reports Server (NTRS)

Parzen, E.

1983-01-01

General ideas of functional statistical inference analysis of one sample and two samples, univariate and bivariate are outlined. ONESAM program is applied to analyze the univariate probability distributions of multi-spectral image data.

Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt)

PubMed Central

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures). PMID:24737994

The Use of Statistical Process Control-Charts for Person-Fit Analysis on Computerized Adaptive Testing. LSAC Research Report Series.

ERIC Educational Resources Information Center

Meijer, Rob R.; van Krimpen-Stoop, Edith M. L. A.

In this study a cumulative-sum (CUSUM) procedure from the theory of Statistical Process Control was modified and applied in the context of person-fit analysis in a computerized adaptive testing (CAT) environment. Six person-fit statistics were proposed using the CUSUM procedure, and three of them could be used to investigate the CAT in online test…

Background Information and User’s Guide for MIL-F-9490

DTIC Science & Technology

1975-01-01

requirements, although different analysis results will apply to each requirement. Basic differences between the two realibility requirements are: MIL-F-8785B...provides the rationale for establishing such limits. The specific risk analysis comprises the same data which formed the average risk analysis , except...statistical analysis will be based on statistical data taken using limited exposure Limes of components and equipment. The exposure times and resulting

Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.

Improving Student Understanding of Spatial Ecology Statistics

ERIC Educational Resources Information Center

Hopkins, Robert, II; Alberts, Halley

2015-01-01

This activity is designed as a primer to teaching population dispersion analysis. The aim is to help improve students' spatial thinking and their understanding of how spatial statistic equations work. Students use simulated data to develop their own statistic and apply that equation to experimental behavioral data for Gambusia affinis (western…

Opportunities for Applied Behavior Analysis in the Total Quality Movement.

ERIC Educational Resources Information Center

Redmon, William K.

1992-01-01

This paper identifies critical components of recent organizational quality improvement programs and specifies how applied behavior analysis can contribute to quality technology. Statistical Process Control and Total Quality Management approaches are compared, and behavior analysts are urged to build their research base and market behavior change…

AASC Recommendations for the Education of an Applied Climatologist

NASA Astrophysics Data System (ADS)

Nielsen-Gammon, J. W.; Stooksbury, D.; Akyuz, A.; Dupigny-Giroux, L.; Hubbard, K. G.; Timofeyeva, M. M.

2011-12-01

The American Association of State Climatologists (AASC) has developed curricular recommendations for the education of future applied and service climatologists. The AASC was founded in 1976. Membership of the AASC includes state climatologists and others who work in state climate offices; climate researchers in academia and educators; applied climatologists in NOAA and other federal agencies; and the private sector. The AASC is the only professional organization dedicated solely to the growth and development of applied and service climatology. The purpose of the recommendations is to offer a framework for existing and developing academic climatology programs. These recommendations are intended to serve as a road map and to help distinguish the educational needs for future applied climatologists from those of operational meteorologists or other scientists and practitioners. While the home department of climatology students may differ from one program to the next, the most essential factor is that students can demonstrate a breadth and depth of understanding in the knowledge and tools needed to be an applied climatologist. Because the training of an applied climatologist requires significant depth and breadth, the Masters degree is recommended as the minimum level of education needed. This presentation will highlight the AASC recommendations. These include a strong foundation in: - climatology (instrumentation and data collection, climate dynamics, physical climatology, synoptic and regional climatology, applied climatology, climate models, etc.) - basic natural sciences and mathematics including calculus, physics, chemistry, and biology/ecology - fundamental atmospheric sciences (atmospheric dynamics, atmospheric thermodynamics, atmospheric radiation, and weather analysis/synoptic meteorology) and - data analysis and spatial analysis (descriptive statistics, statistical methods, multivariate statistics, geostatistics, GIS, etc.). The recommendations also include a secondary area of concentration (agriculture, economics, geography, hydrology, marine sciences, natural resources, policy, etc.) and a major applied climate research component.

Wear behavior of AA 5083/SiC nano-particle metal matrix composite: Statistical analysis

NASA Astrophysics Data System (ADS)

Hussain Idrisi, Amir; Ismail Mourad, Abdel-Hamid; Thekkuden, Dinu Thomas; Christy, John Victor

2018-03-01

This paper reports study on statistical analysis of the wear characteristics of AA5083/SiC nanocomposite. The aluminum matrix composites with different wt % (0%, 1% and 2%) of SiC nanoparticles were fabricated by using stir casting route. The developed composites were used in the manufacturing of spur gears on which the study was conducted. A specially designed test rig was used in testing the wear performance of the gears. The wear was investigated under different conditions of applied load (10N, 20N, and 30N) and operation time (30 mins, 60 mins, 90 mins, and 120mins). The analysis carried out at room temperature under constant speed of 1450 rpm. The wear parameters were optimized by using Taguchi’s method. During this statistical approach, L27 Orthogonal array was selected for the analysis of output. Furthermore, analysis of variance (ANOVA) was used to investigate the influence of applied load, operation time and SiC wt. % on wear behaviour. The wear resistance was analyzed by selecting “smaller is better” characteristics as the objective of the model. From this research, it is observed that experiment time and SiC wt % have the most significant effect on the wear performance followed by the applied load.

[The research protocol VI: How to choose the appropriate statistical test. Inferential statistics].

PubMed

Flores-Ruiz, Eric; Miranda-Novales, María Guadalupe; Villasís-Keever, Miguel Ángel

2017-01-01

The statistical analysis can be divided in two main components: descriptive analysis and inferential analysis. An inference is to elaborate conclusions from the tests performed with the data obtained from a sample of a population. Statistical tests are used in order to establish the probability that a conclusion obtained from a sample is applicable to the population from which it was obtained. However, choosing the appropriate statistical test in general poses a challenge for novice researchers. To choose the statistical test it is necessary to take into account three aspects: the research design, the number of measurements and the scale of measurement of the variables. Statistical tests are divided into two sets, parametric and nonparametric. Parametric tests can only be used if the data show a normal distribution. Choosing the right statistical test will make it easier for readers to understand and apply the results.

The 1993 Mississippi river flood: A one hundred or a one thousand year event?

USGS Publications Warehouse

Malamud, B.D.; Turcotte, D.L.; Barton, C.C.

1996-01-01

Power-law (fractal) extreme-value statistics are applicable to many natural phenomena under a wide variety of circumstances. Data from a hydrologic station in Keokuk, Iowa, shows the great flood of the Mississippi River in 1993 has a recurrence interval on the order of 100 years using power-law statistics applied to partial-duration flood series and on the order of 1,000 years using a log-Pearson type 3 (LP3) distribution applied to annual series. The LP3 analysis is the federally adopted probability distribution for flood-frequency estimation of extreme events. We suggest that power-law statistics are preferable to LP3 analysis. As a further test of the power-law approach we consider paleoflood data from the Colorado River. We compare power-law and LP3 extrapolations of historical data with these paleo-floods. The results are remarkably similar to those obtained for the Mississippi River: Recurrence intervals from power-law statistics applied to Lees Ferry discharge data are generally consistent with inferred 100- and 1,000-year paleofloods, whereas LP3 analysis gives recurrence intervals that are orders of magnitude longer. For both the Keokuk and Lees Ferry gauges, the use of an annual series introduces an artificial curvature in log-log space that leads to an underestimate of severe floods. Power-law statistics are predicting much shorter recurrence intervals than the federally adopted LP3 statistics. We suggest that if power-law behavior is applicable, then the likelihood of severe floods is much higher. More conservative dam designs and land-use restrictions Nay be required.

Statistics for nuclear engineers and scientists. Part 1. Basic statistical inference

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

Beggs, W.J.

1981-02-01

This report is intended for the use of engineers and scientists working in the nuclear industry, especially at the Bettis Atomic Power Laboratory. It serves as the basis for several Bettis in-house statistics courses. The objectives of the report are to introduce the reader to the language and concepts of statistics and to provide a basic set of techniques to apply to problems of the collection and analysis of data. Part 1 covers subjects of basic inference. The subjects include: descriptive statistics; probability; simple inference for normally distributed populations, and for non-normal populations as well; comparison of two populations; themore » analysis of variance; quality control procedures; and linear regression analysis.« less

Twice random, once mixed: applying mixed models to simultaneously analyze random effects of language and participants.

PubMed

Janssen, Dirk P

2012-03-01

Psychologists, psycholinguists, and other researchers using language stimuli have been struggling for more than 30 years with the problem of how to analyze experimental data that contain two crossed random effects (items and participants). The classical analysis of variance does not apply; alternatives have been proposed but have failed to catch on, and a statistically unsatisfactory procedure of using two approximations (known as F(1) and F(2)) has become the standard. A simple and elegant solution using mixed model analysis has been available for 15 years, and recent improvements in statistical software have made mixed models analysis widely available. The aim of this article is to increase the use of mixed models by giving a concise practical introduction and by giving clear directions for undertaking the analysis in the most popular statistical packages. The article also introduces the DJMIXED: add-on package for SPSS, which makes entering the models and reporting their results as straightforward as possible.

HYPOTHESIS SETTING AND ORDER STATISTIC FOR ROBUST GENOMIC META-ANALYSIS.

PubMed

Song, Chi; Tseng, George C

2014-01-01

Meta-analysis techniques have been widely developed and applied in genomic applications, especially for combining multiple transcriptomic studies. In this paper, we propose an order statistic of p-values ( r th ordered p-value, rOP) across combined studies as the test statistic. We illustrate different hypothesis settings that detect gene markers differentially expressed (DE) "in all studies", "in the majority of studies", or "in one or more studies", and specify rOP as a suitable method for detecting DE genes "in the majority of studies". We develop methods to estimate the parameter r in rOP for real applications. Statistical properties such as its asymptotic behavior and a one-sided testing correction for detecting markers of concordant expression changes are explored. Power calculation and simulation show better performance of rOP compared to classical Fisher's method, Stouffer's method, minimum p-value method and maximum p-value method under the focused hypothesis setting. Theoretically, rOP is found connected to the naïve vote counting method and can be viewed as a generalized form of vote counting with better statistical properties. The method is applied to three microarray meta-analysis examples including major depressive disorder, brain cancer and diabetes. The results demonstrate rOP as a more generalizable, robust and sensitive statistical framework to detect disease-related markers.

A study of two statistical methods as applied to shuttle solid rocket booster expenditures

NASA Technical Reports Server (NTRS)

Perlmutter, M.; Huang, Y.; Graves, M.

1974-01-01

The state probability technique and the Monte Carlo technique are applied to finding shuttle solid rocket booster expenditure statistics. For a given attrition rate per launch, the probable number of boosters needed for a given mission of 440 launches is calculated. Several cases are considered, including the elimination of the booster after a maximum of 20 consecutive launches. Also considered is the case where the booster is composed of replaceable components with independent attrition rates. A simple cost analysis is carried out to indicate the number of boosters to build initially, depending on booster costs. Two statistical methods were applied in the analysis: (1) state probability method which consists of defining an appropriate state space for the outcome of the random trials, and (2) model simulation method or the Monte Carlo technique. It was found that the model simulation method was easier to formulate while the state probability method required less computing time and was more accurate.

Parametric distribution approach for flow availability in small hydro potential analysis

NASA Astrophysics Data System (ADS)

Abdullah, Samizee; Basri, Mohd Juhari Mat; Jamaluddin, Zahrul Zamri; Azrulhisham, Engku Ahmad; Othman, Jamel

2016-10-01

Small hydro system is one of the important sources of renewable energy and it has been recognized worldwide as clean energy sources. Small hydropower generation system uses the potential energy in flowing water to produce electricity is often questionable due to inconsistent and intermittent of power generated. Potential analysis of small hydro system which is mainly dependent on the availability of water requires the knowledge of water flow or stream flow distribution. This paper presented the possibility of applying Pearson system for stream flow availability distribution approximation in the small hydro system. By considering the stochastic nature of stream flow, the Pearson parametric distribution approximation was computed based on the significant characteristic of Pearson system applying direct correlation between the first four statistical moments of the distribution. The advantage of applying various statistical moments in small hydro potential analysis will have the ability to analyze the variation shapes of stream flow distribution.

Comparative analysis of ferroelectric domain statistics via nonlinear diffraction in random nonlinear materials.

PubMed

Wang, B; Switowski, K; Cojocaru, C; Roppo, V; Sheng, Y; Scalora, M; Kisielewski, J; Pawlak, D; Vilaseca, R; Akhouayri, H; Krolikowski, W; Trull, J

2018-01-22

We present an indirect, non-destructive optical method for domain statistic characterization in disordered nonlinear crystals having homogeneous refractive index and spatially random distribution of ferroelectric domains. This method relies on the analysis of the wave-dependent spatial distribution of the second harmonic, in the plane perpendicular to the optical axis in combination with numerical simulations. We apply this technique to the characterization of two different media, Calcium Barium Niobate and Strontium Barium Niobate, with drastically different statistical distributions of ferroelectric domains.

STATISTICAL ANALYSIS OF SNAP 10A THERMOELECTRIC CONVERTER ELEMENT PROCESS DEVELOPMENT VARIABLES

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

Fitch, S.H.; Morris, J.W.

1962-12-15

Statistical analysis, primarily analysis of variance, was applied to evaluate several factors involved in the development of suitable fabrication and processing techniques for the production of lead telluride thermoelectric elements for the SNAP 10A energy conversion system. The analysis methods are described as to their application for determining the effects of various processing steps, estabIishing the value of individual operations, and evaluating the significance of test results. The elimination of unnecessary or detrimental processing steps was accomplished and the number of required tests was substantially reduced by application of these statistical methods to the SNAP 10A production development effort. (auth)

Fear and loathing: undergraduate nursing students' experiences of a mandatory course in applied statistics.

PubMed

Hagen, Brad; Awosoga, Oluwagbohunmi A; Kellett, Peter; Damgaard, Marie

2013-04-23

This article describes the results of a qualitative research study evaluating nursing students' experiences of a mandatory course in applied statistics, and the perceived effectiveness of teaching methods implemented during the course. Fifteen nursing students in the third year of a four-year baccalaureate program in nursing participated in focus groups before and after taking the mandatory course in statistics. The interviews were transcribed and analyzed using content analysis to reveal four major themes: (i) "one of those courses you throw out?," (ii) "numbers and terrifying equations," (iii) "first aid for statistics casualties," and (iv) "re-thinking curriculum." Overall, the data revealed that although nursing students initially enter statistics courses with considerable skepticism, fear, and anxiety, there are a number of concrete actions statistics instructors can take to reduce student fear and increase the perceived relevance of courses in statistics.

SSD for R: A Comprehensive Statistical Package to Analyze Single-System Data

ERIC Educational Resources Information Center

Auerbach, Charles; Schudrich, Wendy Zeitlin

2013-01-01

The need for statistical analysis in single-subject designs presents a challenge, as analytical methods that are applied to group comparison studies are often not appropriate in single-subject research. "SSD for R" is a robust set of statistical functions with wide applicability to single-subject research. It is a comprehensive package…

A UNIFYING CONCEPT FOR ASSESSING TOXICOLOGICAL INTERACTIONS: CHANGES IN SLOPE

EPA Science Inventory

Robust statistical methods are important to the evaluation of interactions among chemicals in a mixture. However, different concepts of interaction as applied to the statistical analysis of chemical mixture toxicology data or as used in environmental risk assessment often can ap...

Applied statistics in agricultural, biological, and environmental sciences.

USDA-ARS?s Scientific Manuscript database

Agronomic research often involves measurement and collection of multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate statistical methods encompass the simultaneous analysis of all random variables measured on each experimental or s...

Application of radioisotope XRF and thermoluminescence (TL) dating in investigation of pottery from Tell AL-Kasra archaeological site, Syria.

PubMed

Abboud, R; Issa, H; Abed-Allah, Y D; Bakraji, E H

2015-11-01

Statistical analysis based on chemical composition, using radioisotope X-ray fluorescence, have been applied on 39 ancient pottery fragments coming from the excavation at Tell Al-Kasra archaeological site, Syria. Three groups were defined by applying Cluster and Factor analysis statistical methods. Thermoluminescence (TL) dating was investigated on three sherds taken from the bathroom (hammam) on the site. Multiple aliquot additive dose (MAAD) was used to estimate the paleodose value, and the gamma spectrometry was used to estimate the dose rate. The average age was found to be 715±36 year. Copyright © 2015 Elsevier Ltd. All rights reserved.

Analysis of Sensitivity Experiments - An Expanded Primer

DTIC Science & Technology

2017-03-08

diehard practitioners. The difficulty associated with mastering statistical inference presents a true dilemma. Statistics is an extremely applied...lost, perhaps forever. In other words, when on this safari, you need a guide. This report is designed to be a guide, of sorts. It focuses on analytical...estimated accurately if our analysis is to have real meaning. For this reason, the sensitivity test procedure is designed to concentrate measurements

Statistical Signal Models and Algorithms for Image Analysis

DTIC Science & Technology

1984-10-25

In this report, two-dimensional stochastic linear models are used in developing algorithms for image analysis such as classification, segmentation, and object detection in images characterized by textured backgrounds. These models generate two-dimensional random processes as outputs to which statistical inference procedures can naturally be applied. A common thread throughout our algorithms is the interpretation of the inference procedures in terms of linear prediction

Non-Destructive Measurement Methods (Neutron-, X-ray Radiography, Vibration Diagnostics and Ultrasound) in the Inspection of Helicopter Rotor Blades

DTIC Science & Technology

2005-04-01

the radiography gauging. In addition to the Statistical Energy Analysis (SEA) measurement a small exciter table (BK4810) and impedance head (BK 8000... Statistical Energy Analysis ; 7th Conf. on Vehicle System Dynamics, Identification and Anomalies (VSDIA2000), 6-8 Nov. 2000 Budapest, Proc. pp. 491-493... Energy Analysis (SEA) and Ultrasound Test. (UT) were concurrently applied. These methods collect accessory information on the objects under inspection

Uncertainty Analysis for DAM Projects.

DTIC Science & Technology

1987-09-01

overwhelming majority of articles published on the use of statistical methodology for geotechnical engineering focus on performance predictions and design ...Results of the present study do not support the adoption of more esoteric statistical procedures except on a special case basis or in research ...influence that recommended statistical procedures might have had on the Carters Project, had they been applied during planning and design phases

Analysis of Statistical Methods and Errors in the Articles Published in the Korean Journal of Pain

PubMed Central

Yim, Kyoung Hoon; Han, Kyoung Ah; Park, Soo Young

2010-01-01

Background Statistical analysis is essential in regard to obtaining objective reliability for medical research. However, medical researchers do not have enough statistical knowledge to properly analyze their study data. To help understand and potentially alleviate this problem, we have analyzed the statistical methods and errors of articles published in the Korean Journal of Pain (KJP), with the intention to improve the statistical quality of the journal. Methods All the articles, except case reports and editorials, published from 2004 to 2008 in the KJP were reviewed. The types of applied statistical methods and errors in the articles were evaluated. Results One hundred and thirty-nine original articles were reviewed. Inferential statistics and descriptive statistics were used in 119 papers and 20 papers, respectively. Only 20.9% of the papers were free from statistical errors. The most commonly adopted statistical method was the t-test (21.0%) followed by the chi-square test (15.9%). Errors of omission were encountered 101 times in 70 papers. Among the errors of omission, "no statistics used even though statistical methods were required" was the most common (40.6%). The errors of commission were encountered 165 times in 86 papers, among which "parametric inference for nonparametric data" was the most common (33.9%). Conclusions We found various types of statistical errors in the articles published in the KJP. This suggests that meticulous attention should be given not only in the applying statistical procedures but also in the reviewing process to improve the value of the article. PMID:20552071

Circularly-symmetric complex normal ratio distribution for scalar transmissibility functions. Part III: Application to statistical modal analysis

NASA Astrophysics Data System (ADS)

Yan, Wang-Ji; Ren, Wei-Xin

2018-01-01

This study applies the theoretical findings of circularly-symmetric complex normal ratio distribution Yan and Ren (2016) [1,2] to transmissibility-based modal analysis from a statistical viewpoint. A probabilistic model of transmissibility function in the vicinity of the resonant frequency is formulated in modal domain, while some insightful comments are offered. It theoretically reveals that the statistics of transmissibility function around the resonant frequency is solely dependent on 'noise-to-signal' ratio and mode shapes. As a sequel to the development of the probabilistic model of transmissibility function in modal domain, this study poses the process of modal identification in the context of Bayesian framework by borrowing a novel paradigm. Implementation issues unique to the proposed approach are resolved by Lagrange multiplier approach. Also, this study explores the possibility of applying Bayesian analysis in distinguishing harmonic components and structural ones. The approaches are verified through simulated data and experimentally testing data. The uncertainty behavior due to variation of different factors is also discussed in detail.

Multi-trait analysis of genome-wide association summary statistics using MTAG.

PubMed

Turley, Patrick; Walters, Raymond K; Maghzian, Omeed; Okbay, Aysu; Lee, James J; Fontana, Mark Alan; Nguyen-Viet, Tuan Anh; Wedow, Robbee; Zacher, Meghan; Furlotte, Nicholas A; Magnusson, Patrik; Oskarsson, Sven; Johannesson, Magnus; Visscher, Peter M; Laibson, David; Cesarini, David; Neale, Benjamin M; Benjamin, Daniel J

2018-02-01

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff  = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

IMPLEMENTATION AND VALIDATION OF STATISTICAL TESTS IN RESEARCH'S SOFTWARE HELPING DATA COLLECTION AND PROTOCOLS ANALYSIS IN SURGERY.

PubMed

Kuretzki, Carlos Henrique; Campos, Antônio Carlos Ligocki; Malafaia, Osvaldo; Soares, Sandramara Scandelari Kusano de Paula; Tenório, Sérgio Bernardo; Timi, Jorge Rufino Ribas

2016-03-01

The use of information technology is often applied in healthcare. With regard to scientific research, the SINPE(c) - Integrated Electronic Protocols was created as a tool to support researchers, offering clinical data standardization. By the time, SINPE(c) lacked statistical tests obtained by automatic analysis. Add to SINPE(c) features for automatic realization of the main statistical methods used in medicine . The study was divided into four topics: check the interest of users towards the implementation of the tests; search the frequency of their use in health care; carry out the implementation; and validate the results with researchers and their protocols. It was applied in a group of users of this software in their thesis in the strict sensu master and doctorate degrees in one postgraduate program in surgery. To assess the reliability of the statistics was compared the data obtained both automatically by SINPE(c) as manually held by a professional in statistics with experience with this type of study. There was concern for the use of automatic statistical tests, with good acceptance. The chi-square, Mann-Whitney, Fisher and t-Student were considered as tests frequently used by participants in medical studies. These methods have been implemented and thereafter approved as expected. The incorporation of the automatic SINPE (c) Statistical Analysis was shown to be reliable and equal to the manually done, validating its use as a research tool for medical research.

Metamodels for Computer-Based Engineering Design: Survey and Recommendations

NASA Technical Reports Server (NTRS)

Simpson, Timothy W.; Peplinski, Jesse; Koch, Patrick N.; Allen, Janet K.

1997-01-01

The use of statistical techniques to build approximations of expensive computer analysis codes pervades much of todays engineering design. These statistical approximations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and concept exploration. In this paper we review several of these techniques including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We survey their existing application in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of statistical approximation techniques in given situations and how common pitfalls can be avoided.

Examining a Terrorist Network Using Contingency Table Analysis

DTIC Science & Technology

2011-08-01

Mathematics and Statistics, with a minor in Actuarial Science. This is my second year as a summer student at the U.S. Army Research Laboratory (ARL...After graduation, I plan on either attending graduate school to concentrate in applied statistics or becoming a mathematical statistician for the

Statistical analysis of global horizontal solar irradiation GHI in Fez city, Morocco

NASA Astrophysics Data System (ADS)

Bounoua, Z.; Mechaqrane, A.

2018-05-01

An accurate knowledge of the solar energy reaching the ground is necessary for sizing and optimizing the performances of solar installations. This paper describes a statistical analysis of the global horizontal solar irradiation (GHI) at Fez city, Morocco. For better reliability, we have first applied a set of check procedures to test the quality of hourly GHI measurements. We then eliminate the erroneous values which are generally due to measurement or the cosine effect errors. Statistical analysis show that the annual mean daily values of GHI is of approximately 5 kWh/m²/day. Daily monthly mean values and other parameter are also calculated.

Obscure phenomena in statistical analysis of quantitative structure-activity relationships. Part 1: Multicollinearity of physicochemical descriptors.

PubMed

Mager, P P; Rothe, H

1990-10-01

Multicollinearity of physicochemical descriptors leads to serious consequences in quantitative structure-activity relationship (QSAR) analysis, such as incorrect estimators and test statistics of regression coefficients of the ordinary least-squares (OLS) model applied usually to QSARs. Beside the diagnosis of the known simple collinearity, principal component regression analysis (PCRA) also allows the diagnosis of various types of multicollinearity. Only if the absolute values of PCRA estimators are order statistics that decrease monotonically, the effects of multicollinearity can be circumvented. Otherwise, obscure phenomena may be observed, such as good data recognition but low predictive model power of a QSAR model.

Scientific computations section monthly report, November 1993

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

Buckner, M.R.

1993-12-30

This progress report from the Savannah River Technology Center contains abstracts from papers from the computational modeling, applied statistics, applied physics, experimental thermal hydraulics, and packaging and transportation groups. Specific topics covered include: engineering modeling and process simulation, criticality methods and analysis, plutonium disposition.

CORSSA: The Community Online Resource for Statistical Seismicity Analysis

USGS Publications Warehouse

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.

A phylogenetic transform enhances analysis of compositional microbiota data.

PubMed

Silverman, Justin D; Washburne, Alex D; Mukherjee, Sayan; David, Lawrence A

2017-02-15

Surveys of microbial communities (microbiota), typically measured as relative abundance of species, have illustrated the importance of these communities in human health and disease. Yet, statistical artifacts commonly plague the analysis of relative abundance data. Here, we introduce the PhILR transform, which incorporates microbial evolutionary models with the isometric log-ratio transform to allow off-the-shelf statistical tools to be safely applied to microbiota surveys. We demonstrate that analyses of community-level structure can be applied to PhILR transformed data with performance on benchmarks rivaling or surpassing standard tools. Additionally, by decomposing distance in the PhILR transformed space, we identified neighboring clades that may have adapted to distinct human body sites. Decomposing variance revealed that covariation of bacterial clades within human body sites increases with phylogenetic relatedness. Together, these findings illustrate how the PhILR transform combines statistical and phylogenetic models to overcome compositional data challenges and enable evolutionary insights relevant to microbial communities.

Analyzing a Mature Software Inspection Process Using Statistical Process Control (SPC)

NASA Technical Reports Server (NTRS)

Barnard, Julie; Carleton, Anita; Stamper, Darrell E. (Technical Monitor)

1999-01-01

This paper presents a cooperative effort where the Software Engineering Institute and the Space Shuttle Onboard Software Project could experiment applying Statistical Process Control (SPC) analysis to inspection activities. The topics include: 1) SPC Collaboration Overview; 2) SPC Collaboration Approach and Results; and 3) Lessons Learned.

Genetic structure of populations and differentiation in forest trees

Treesearch

Raymond P. Guries; F. Thomas Ledig

1981-01-01

Electrophoretic techniques permit population biologists to analyze genetic structure of natural populations by using large numbers of allozyme loci. Several methods of analysis have been applied to allozyme data, including chi-square contingency tests, F-statistics, and genetic distance. This paper compares such statistics for pitch pine (Pinus rigida...

Improving Markov Chain Models for Road Profiles Simulation via Definition of States

DTIC Science & Technology

2012-04-01

wavelet transform in pavement profile analysis," Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, vol. 47, no. 4...34Estimating Markov Transition Probabilities from Micro -Unit Data," Journal of the Royal Statistical Society. Series C (Applied Statistics), pp. 355-371

Security of statistical data bases: invasion of privacy through attribute correlational modeling

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

Palley, M.A.

This study develops, defines, and applies a statistical technique for the compromise of confidential information in a statistical data base. Attribute Correlational Modeling (ACM) recognizes that the information contained in a statistical data base represents real world statistical phenomena. As such, ACM assumes correlational behavior among the database attributes. ACM proceeds to compromise confidential information through creation of a regression model, where the confidential attribute is treated as the dependent variable. The typical statistical data base may preclude the direct application of regression. In this scenario, the research introduces the notion of a synthetic data base, created through legitimate queriesmore » of the actual data base, and through proportional random variation of responses to these queries. The synthetic data base is constructed to resemble the actual data base as closely as possible in a statistical sense. ACM then applies regression analysis to the synthetic data base, and utilizes the derived model to estimate confidential information in the actual database.« less

[Evaluation of using statistical methods in selected national medical journals].

PubMed

Sych, Z

1996-01-01

The paper covers the performed evaluation of frequency with which the statistical methods were applied in analyzed works having been published in six selected, national medical journals in the years 1988-1992. For analysis the following journals were chosen, namely: Klinika Oczna, Medycyna Pracy, Pediatria Polska, Polski Tygodnik Lekarski, Roczniki Państwowego Zakładu Higieny, Zdrowie Publiczne. Appropriate number of works up to the average in the remaining medical journals was randomly selected from respective volumes of Pol. Tyg. Lek. The studies did not include works wherein the statistical analysis was not implemented, which referred both to national and international publications. That exemption was also extended to review papers, casuistic ones, reviews of books, handbooks, monographies, reports from scientific congresses, as well as papers on historical topics. The number of works was defined in each volume. Next, analysis was performed to establish the mode of finding out a suitable sample in respective studies, differentiating two categories: random and target selections. Attention was also paid to the presence of control sample in the individual works. In the analysis attention was also focussed on the existence of sample characteristics, setting up three categories: complete, partial and lacking. In evaluating the analyzed works an effort was made to present the results of studies in tables and figures (Tab. 1, 3). Analysis was accomplished with regard to the rate of employing statistical methods in analyzed works in relevant volumes of six selected, national medical journals for the years 1988-1992, simultaneously determining the number of works, in which no statistical methods were used. Concurrently the frequency of applying the individual statistical methods was analyzed in the scrutinized works. Prominence was given to fundamental statistical methods in the field of descriptive statistics (measures of position, measures of dispersion) as well as most important methods of mathematical statistics such as parametric tests of significance, analysis of variance (in single and dual classifications). non-parametric tests of significance, correlation and regression. The works, in which use was made of either multiple correlation or multiple regression or else more complex methods of studying the relationship for two or more numbers of variables, were incorporated into the works whose statistical methods were constituted by correlation and regression as well as other methods, e.g. statistical methods being used in epidemiology (coefficients of incidence and morbidity, standardization of coefficients, survival tables) factor analysis conducted by Jacobi-Hotellng's method, taxonomic methods and others. On the basis of the performed studies it has been established that the frequency of employing statistical methods in the six selected national, medical journals in the years 1988-1992 was 61.1-66.0% of the analyzed works (Tab. 3), and they generally were almost similar to the frequency provided in English language medical journals. On a whole, no significant differences were disclosed in the frequency of applied statistical methods (Tab. 4) as well as in frequency of random tests (Tab. 3) in the analyzed works, appearing in the medical journals in respective years 1988-1992. The most frequently used statistical methods in analyzed works for 1988-1992 were the measures of position 44.2-55.6% and measures of dispersion 32.5-38.5% as well as parametric tests of significance 26.3-33.1% of the works analyzed (Tab. 4). For the purpose of increasing the frequency and reliability of the used statistical methods, the didactics should be widened in the field of biostatistics at medical studies and postgraduation training designed for physicians and scientific-didactic workers.

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.

14 CFR 417.203 - Compliance.

Code of Federal Regulations, 2012 CFR

2012-01-01

... analysis method is based on accurate data and scientific principles and is statistically valid. The FAA... safety analysis must also meet the requirements for methods of analysis contained in appendices A and B... from an identical or similar launch if the analysis still applies to the later launch. (b) Method of...

14 CFR 417.203 - Compliance.

Code of Federal Regulations, 2014 CFR

2014-01-01

... analysis method is based on accurate data and scientific principles and is statistically valid. The FAA... safety analysis must also meet the requirements for methods of analysis contained in appendices A and B... from an identical or similar launch if the analysis still applies to the later launch. (b) Method of...

14 CFR 417.203 - Compliance.

Code of Federal Regulations, 2013 CFR

2013-01-01

... analysis method is based on accurate data and scientific principles and is statistically valid. The FAA... safety analysis must also meet the requirements for methods of analysis contained in appendices A and B... from an identical or similar launch if the analysis still applies to the later launch. (b) Method of...

Independent component analysis for automatic note extraction from musical trills

NASA Astrophysics Data System (ADS)

Brown, Judith C.; Smaragdis, Paris

2004-05-01

The method of principal component analysis, which is based on second-order statistics (or linear independence), has long been used for redundancy reduction of audio data. The more recent technique of independent component analysis, enforcing much stricter statistical criteria based on higher-order statistical independence, is introduced and shown to be far superior in separating independent musical sources. This theory has been applied to piano trills and a database of trill rates was assembled from experiments with a computer-driven piano, recordings of a professional pianist, and commercially available compact disks. The method of independent component analysis has thus been shown to be an outstanding, effective means of automatically extracting interesting musical information from a sea of redundant data.

[Application of Stata software to test heterogeneity in meta-analysis method].

PubMed

Wang, Dan; Mou, Zhen-yun; Zhai, Jun-xia; Zong, Hong-xia; Zhao, Xiao-dong

2008-07-01

To introduce the application of Stata software to heterogeneity test in meta-analysis. A data set was set up according to the example in the study, and the corresponding commands of the methods in Stata 9 software were applied to test the example. The methods used were Q-test and I2 statistic attached to the fixed effect model forest plot, H statistic and Galbraith plot. The existence of the heterogeneity among studies could be detected by Q-test and H statistic and the degree of the heterogeneity could be detected by I2 statistic. The outliers which were the sources of the heterogeneity could be spotted from the Galbraith plot. Heterogeneity test in meta-analysis can be completed by the four methods in Stata software simply and quickly. H and I2 statistics are more robust, and the outliers of the heterogeneity can be clearly seen in the Galbraith plot among the four methods.

Recovering incomplete data using Statistical Multiple Imputations (SMI): a case study in environmental chemistry.

PubMed

Mercer, Theresa G; Frostick, Lynne E; Walmsley, Anthony D

2011-10-15

This paper presents a statistical technique that can be applied to environmental chemistry data where missing values and limit of detection levels prevent the application of statistics. A working example is taken from an environmental leaching study that was set up to determine if there were significant differences in levels of leached arsenic (As), chromium (Cr) and copper (Cu) between lysimeters containing preservative treated wood waste and those containing untreated wood. Fourteen lysimeters were setup and left in natural conditions for 21 weeks. The resultant leachate was analysed by ICP-OES to determine the As, Cr and Cu concentrations. However, due to the variation inherent in each lysimeter combined with the limits of detection offered by ICP-OES, the collected quantitative data was somewhat incomplete. Initial data analysis was hampered by the number of 'missing values' in the data. To recover the dataset, the statistical tool of Statistical Multiple Imputation (SMI) was applied, and the data was re-analysed successfully. It was demonstrated that using SMI did not affect the variance in the data, but facilitated analysis of the complete dataset. Copyright © 2011 Elsevier B.V. All rights reserved.

Analysis of Antarctic Remote-Site Automatic Weather Station Data for Period January 1979 - February 1980.

DTIC Science & Technology

1982-06-01

usefulness to the Untted States Antarctic mission as managed by the National Science Foundation. Various statistical measures were applied to the reported... statistical procedures that would evolve a general meteorological picture of each of these remote sites. Primary texts used as a basis for...processed by station for monthly, seasonal and annual statistics , as appropriate. The following outlines the evaluations completed for both

Examination of two methods for statistical analysis of data with magnitude and direction emphasizing vestibular research applications

NASA Technical Reports Server (NTRS)

Calkins, D. S.

1998-01-01

When the dependent (or response) variable response variable in an experiment has direction and magnitude, one approach that has been used for statistical analysis involves splitting magnitude and direction and applying univariate statistical techniques to the components. However, such treatment of quantities with direction and magnitude is not justifiable mathematically and can lead to incorrect conclusions about relationships among variables and, as a result, to flawed interpretations. This note discusses a problem with that practice and recommends mathematically correct procedures to be used with dependent variables that have direction and magnitude for 1) computation of mean values, 2) statistical contrasts of and confidence intervals for means, and 3) correlation methods.

Some Epistemological Considerations Concerning Quantitative Analysis

ERIC Educational Resources Information Center

Dobrescu, Emilian

2008-01-01

This article presents the author's address at the 2007 "Journal of Applied Quantitative Methods" ("JAQM") prize awarding festivity. The festivity was included in the opening of the 4th International Conference on Applied Statistics, November 22, 2008, Bucharest, Romania. In the address, the author reflects on three theses that…

Integrated Analysis of Pharmacologic, Clinical, and SNP Microarray Data using Projection onto the Most Interesting Statistical Evidence with Adaptive Permutation Testing

PubMed Central

Pounds, Stan; Cao, Xueyuan; Cheng, Cheng; Yang, Jun; Campana, Dario; Evans, William E.; Pui, Ching-Hon; Relling, Mary V.

2010-01-01

Powerful methods for integrated analysis of multiple biological data sets are needed to maximize interpretation capacity and acquire meaningful knowledge. We recently developed Projection Onto the Most Interesting Statistical Evidence (PROMISE). PROMISE is a statistical procedure that incorporates prior knowledge about the biological relationships among endpoint variables into an integrated analysis of microarray gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical, and genome-wide genotype data that incorporating knowledge about the biological relationships among pharmacologic and clinical response data. An efficient permutation-testing algorithm is introduced so that statistical calculations are computationally feasible in this higher-dimension setting. The new method is applied to a pediatric leukemia data set. The results clearly indicate that PROMISE is a powerful statistical tool for identifying genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables. PMID:21516175

[Bayesian statistics in medicine -- part II: main applications and inference].

PubMed

Montomoli, C; Nichelatti, M

2008-01-01

Bayesian statistics is not only used when one is dealing with 2-way tables, but it can be used for inferential purposes. Using the basic concepts presented in the first part, this paper aims to give a simple overview of Bayesian methods by introducing its foundation (Bayes' theorem) and then applying this rule to a very simple practical example; whenever possible, the elementary processes at the basis of analysis are compared to those of frequentist (classical) statistical analysis. The Bayesian reasoning is naturally connected to medical activity, since it appears to be quite similar to a diagnostic process.

Statistical and Economic Techniques for Site-specific Nematode Management.

PubMed

Liu, Zheng; Griffin, Terry; Kirkpatrick, Terrence L

2014-03-01

Recent advances in precision agriculture technologies and spatial statistics allow realistic, site-specific estimation of nematode damage to field crops and provide a platform for the site-specific delivery of nematicides within individual fields. This paper reviews the spatial statistical techniques that model correlations among neighboring observations and develop a spatial economic analysis to determine the potential of site-specific nematicide application. The spatial econometric methodology applied in the context of site-specific crop yield response contributes to closing the gap between data analysis and realistic site-specific nematicide recommendations and helps to provide a practical method of site-specifically controlling nematodes.

Cognition of and Demand for Education and Teaching in Medical Statistics in China: A Systematic Review and Meta-Analysis

PubMed Central

Li, Gaoming; Yi, Dali; Wu, Xiaojiao; Liu, Xiaoyu; Zhang, Yanqi; Liu, Ling; Yi, Dong

2015-01-01

Background Although a substantial number of studies focus on the teaching and application of medical statistics in China, few studies comprehensively evaluate the recognition of and demand for medical statistics. In addition, the results of these various studies differ and are insufficiently comprehensive and systematic. Objectives This investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China. Methods We performed a comprehensive database search related to the cognition of and demand for medical statistics from January 2007 to July 2014 and conducted a meta-analysis of non-controlled studies with sub-group analysis for undergraduates, graduates, and medical staff. Results There are substantial differences with respect to the cognition of theory in medical statistics among undergraduates (73.5%), graduates (60.7%), and medical staff (39.6%). The demand for theory in medical statistics is high among graduates (94.6%), undergraduates (86.1%), and medical staff (88.3%). Regarding specific statistical methods, the cognition of basic statistical methods is higher than of advanced statistical methods. The demand for certain advanced statistical methods, including (but not limited to) multiple analysis of variance (ANOVA), multiple linear regression, and logistic regression, is higher than that for basic statistical methods. The use rates of the Statistical Package for the Social Sciences (SPSS) software and statistical analysis software (SAS) are only 55% and 15%, respectively. Conclusion The overall statistical competence of undergraduates, graduates, and medical staff is insufficient, and their ability to practically apply their statistical knowledge is limited, which constitutes an unsatisfactory state of affairs for medical statistics education. Because the demand for skills in this area is increasing, the need to reform medical statistics education in China has become urgent. PMID:26053876

Cognition of and Demand for Education and Teaching in Medical Statistics in China: A Systematic Review and Meta-Analysis.

PubMed

Wu, Yazhou; Zhou, Liang; Li, Gaoming; Yi, Dali; Wu, Xiaojiao; Liu, Xiaoyu; Zhang, Yanqi; Liu, Ling; Yi, Dong

2015-01-01

Although a substantial number of studies focus on the teaching and application of medical statistics in China, few studies comprehensively evaluate the recognition of and demand for medical statistics. In addition, the results of these various studies differ and are insufficiently comprehensive and systematic. This investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China. We performed a comprehensive database search related to the cognition of and demand for medical statistics from January 2007 to July 2014 and conducted a meta-analysis of non-controlled studies with sub-group analysis for undergraduates, graduates, and medical staff. There are substantial differences with respect to the cognition of theory in medical statistics among undergraduates (73.5%), graduates (60.7%), and medical staff (39.6%). The demand for theory in medical statistics is high among graduates (94.6%), undergraduates (86.1%), and medical staff (88.3%). Regarding specific statistical methods, the cognition of basic statistical methods is higher than of advanced statistical methods. The demand for certain advanced statistical methods, including (but not limited to) multiple analysis of variance (ANOVA), multiple linear regression, and logistic regression, is higher than that for basic statistical methods. The use rates of the Statistical Package for the Social Sciences (SPSS) software and statistical analysis software (SAS) are only 55% and 15%, respectively. The overall statistical competence of undergraduates, graduates, and medical staff is insufficient, and their ability to practically apply their statistical knowledge is limited, which constitutes an unsatisfactory state of affairs for medical statistics education. Because the demand for skills in this area is increasing, the need to reform medical statistics education in China has become urgent.

Theory and analysis of statistical discriminant techniques as applied to remote sensing data

NASA Technical Reports Server (NTRS)

Odell, P. L.

1973-01-01

Classification of remote earth resources sensing data according to normed exponential density statistics is reported. The use of density models appropriate for several physical situations provides an exact solution for the probabilities of classifications associated with the Bayes discriminant procedure even when the covariance matrices are unequal.

Statistical description of tectonic motions

NASA Technical Reports Server (NTRS)

Agnew, Duncan Carr

1993-01-01

This report summarizes investigations regarding tectonic motions. The topics discussed include statistics of crustal deformation, Earth rotation studies, using multitaper spectrum analysis techniques applied to both space-geodetic data and conventional astrometric estimates of the Earth's polar motion, and the development, design, and installation of high-stability geodetic monuments for use with the global positioning system.

Statistical properties of DNA sequences

NASA Technical Reports Server (NTRS)

Peng, C. K.; Buldyrev, S. V.; Goldberger, A. L.; Havlin, S.; Mantegna, R. N.; Simons, M.; Stanley, H. E.

1995-01-01

We review evidence supporting the idea that the DNA sequence in genes containing non-coding regions is correlated, and that the correlation is remarkably long range--indeed, nucleotides thousands of base pairs distant are correlated. We do not find such a long-range correlation in the coding regions of the gene. We resolve the problem of the "non-stationarity" feature of the sequence of base pairs by applying a new algorithm called detrended fluctuation analysis (DFA). We address the claim of Voss that there is no difference in the statistical properties of coding and non-coding regions of DNA by systematically applying the DFA algorithm, as well as standard FFT analysis, to every DNA sequence (33301 coding and 29453 non-coding) in the entire GenBank database. Finally, we describe briefly some recent work showing that the non-coding sequences have certain statistical features in common with natural and artificial languages. Specifically, we adapt to DNA the Zipf approach to analyzing linguistic texts. These statistical properties of non-coding sequences support the possibility that non-coding regions of DNA may carry biological information.

Quantifying the evolution of flow boiling bubbles by statistical testing and image analysis: toward a general model.

PubMed

Xiao, Qingtai; Xu, Jianxin; Wang, Hua

2016-08-16

A new index, the estimate of the error variance, which can be used to quantify the evolution of the flow patterns when multiphase components or tracers are difficultly distinguishable, was proposed. The homogeneity degree of the luminance space distribution behind the viewing windows in the direct contact boiling heat transfer process was explored. With image analysis and a linear statistical model, the F-test of the statistical analysis was used to test whether the light was uniform, and a non-linear method was used to determine the direction and position of a fixed source light. The experimental results showed that the inflection point of the new index was approximately equal to the mixing time. The new index has been popularized and applied to a multiphase macro mixing process by top blowing in a stirred tank. Moreover, a general quantifying model was introduced for demonstrating the relationship between the flow patterns of the bubble swarms and heat transfer. The results can be applied to investigate other mixing processes that are very difficult to recognize the target.

Quantifying the evolution of flow boiling bubbles by statistical testing and image analysis: toward a general model

PubMed Central

Xiao, Qingtai; Xu, Jianxin; Wang, Hua

2016-01-01

A new index, the estimate of the error variance, which can be used to quantify the evolution of the flow patterns when multiphase components or tracers are difficultly distinguishable, was proposed. The homogeneity degree of the luminance space distribution behind the viewing windows in the direct contact boiling heat transfer process was explored. With image analysis and a linear statistical model, the F-test of the statistical analysis was used to test whether the light was uniform, and a non-linear method was used to determine the direction and position of a fixed source light. The experimental results showed that the inflection point of the new index was approximately equal to the mixing time. The new index has been popularized and applied to a multiphase macro mixing process by top blowing in a stirred tank. Moreover, a general quantifying model was introduced for demonstrating the relationship between the flow patterns of the bubble swarms and heat transfer. The results can be applied to investigate other mixing processes that are very difficult to recognize the target. PMID:27527065

Analyzing Randomized Controlled Interventions: Three Notes for Applied Linguists

ERIC Educational Resources Information Center

Vanhove, Jan

2015-01-01

I discuss three common practices that obfuscate or invalidate the statistical analysis of randomized controlled interventions in applied linguistics. These are (a) checking whether randomization produced groups that are balanced on a number of possibly relevant covariates, (b) using repeated measures ANOVA to analyze pretest-posttest designs, and…

Generalized Majority Logic Criterion to Analyze the Statistical Strength of S-Boxes

NASA Astrophysics Data System (ADS)

Hussain, Iqtadar; Shah, Tariq; Gondal, Muhammad Asif; Mahmood, Hasan

2012-05-01

The majority logic criterion is applicable in the evaluation process of substitution boxes used in the advanced encryption standard (AES). The performance of modified or advanced substitution boxes is predicted by processing the results of statistical analysis by the majority logic criteria. In this paper, we use the majority logic criteria to analyze some popular and prevailing substitution boxes used in encryption processes. In particular, the majority logic criterion is applied to AES, affine power affine (APA), Gray, Lui J, residue prime, S8 AES, Skipjack, and Xyi substitution boxes. The majority logic criterion is further extended into a generalized majority logic criterion which has a broader spectrum of analyzing the effectiveness of substitution boxes in image encryption applications. The integral components of the statistical analyses used for the generalized majority logic criterion are derived from results of entropy analysis, contrast analysis, correlation analysis, homogeneity analysis, energy analysis, and mean of absolute deviation (MAD) analysis.

Inhibition of Orthopaedic Implant Infections by Immunomodulatory Effects of Host Defense Peptides

DTIC Science & Technology

2014-12-01

significance was determined by t- tests or by one-way analysis of variance (ANOVA) followed by Bonferroni post hoc tests in experiments with multiple...groups. Non- parametric Mann-Whitney tests , Kruskal-Wallis ANOVA followed by Newman-Kuels post hoc tests , or van Elteren’s two-way tests were applied to...in D, and black symbols in A), statistical analysis was by one-way ANOVA followed by Bonferroni versus control, post hoc tests . Otherwise, statistical

Statistical analysis of activation and reaction energies with quasi-variational coupled-cluster theory

NASA Astrophysics Data System (ADS)

Black, Joshua A.; Knowles, Peter J.

2018-06-01

The performance of quasi-variational coupled-cluster (QV) theory applied to the calculation of activation and reaction energies has been investigated. A statistical analysis of results obtained for six different sets of reactions has been carried out, and the results have been compared to those from standard single-reference methods. In general, the QV methods lead to increased activation energies and larger absolute reaction energies compared to those obtained with traditional coupled-cluster theory.

Comparisons of non-Gaussian statistical models in DNA methylation analysis.

PubMed

Ma, Zhanyu; Teschendorff, Andrew E; Yu, Hong; Taghia, Jalil; Guo, Jun

2014-06-16

As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance.

Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis

PubMed Central

Ma, Zhanyu; Teschendorff, Andrew E.; Yu, Hong; Taghia, Jalil; Guo, Jun

2014-01-01

As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance. PMID:24937687

Kolmogorov-Smirnov statistical test for analysis of ZAP-70 expression in B-CLL, compared with quantitative PCR and IgV(H) mutation status.

PubMed

Van Bockstaele, Femke; Janssens, Ann; Piette, Anne; Callewaert, Filip; Pede, Valerie; Offner, Fritz; Verhasselt, Bruno; Philippé, Jan

2006-07-15

ZAP-70 has been proposed as a surrogate marker for immunoglobulin heavy-chain variable region (IgV(H)) mutation status, which is known as a prognostic marker in B-cell chronic lymphocytic leukemia (CLL). The flow cytometric analysis of ZAP-70 suffers from difficulties in standardization and interpretation. We applied the Kolmogorov-Smirnov (KS) statistical test to make analysis more straightforward. We examined ZAP-70 expression by flow cytometry in 53 patients with CLL. Analysis was performed as initially described by Crespo et al. (New England J Med 2003; 348:1764-1775) and alternatively by application of the KS statistical test comparing T cells with B cells. Receiver-operating-characteristics (ROC)-curve analyses were performed to determine the optimal cut-off values for ZAP-70 measured by the two approaches. ZAP-70 protein expression was compared with ZAP-70 mRNA expression measured by a quantitative PCR (qPCR) and with the IgV(H) mutation status. Both flow cytometric analyses correlated well with the molecular technique and proved to be of equal value in predicting the IgV(H) mutation status. Applying the KS test is reproducible, simple, straightforward, and overcomes a number of difficulties encountered in the Crespo-method. The KS statistical test is an essential part of the software delivered with modern routine analytical flow cytometers and is well suited for analysis of ZAP-70 expression in CLL. (c) 2006 International Society for Analytical Cytology.

Use of statistical study methods for the analysis of the results of the imitation modeling of radiation transfer

NASA Astrophysics Data System (ADS)

Alekseenko, M. A.; Gendrina, I. Yu.

2017-11-01

Recently, due to the abundance of various types of observational data in the systems of vision through the atmosphere and the need for their processing, the use of various methods of statistical research in the study of such systems as correlation-regression analysis, dynamic series, variance analysis, etc. is actual. We have attempted to apply elements of correlation-regression analysis for the study and subsequent prediction of the patterns of radiation transfer in these systems same as in the construction of radiation models of the atmosphere. In this paper, we present some results of statistical processing of the results of numerical simulation of the characteristics of vision systems through the atmosphere obtained with the help of a special software package.1

[Statistical analysis of German radiologic periodicals: developmental trends in the last 10 years].

PubMed

Golder, W

1999-09-01

To identify which statistical tests are applied in German radiological publications, to what extent their use has changed during the last decade, and which factors might be responsible for this development. The major articles published in "ROFO" and "DER RADIOLOGE" during 1988, 1993 and 1998 were reviewed for statistical content. The contributions were classified by principal focus and radiological subspecialty. The methods used were assigned to descriptive, basal and advanced statistics. Sample size, significance level and power were established. The use of experts' assistance was monitored. Finally, we calculated the so-called cumulative accessibility of the publications. 525 contributions were found to be eligible. In 1988, 87% used descriptive statistics only, 12.5% basal, and 0.5% advanced statistics. The corresponding figures in 1993 and 1998 are 62 and 49%, 32 and 41%, and 6 and 10%, respectively. Statistical techniques were most likely to be used in research on musculoskeletal imaging and articles dedicated to MRI. Six basic categories of statistical methods account for the complete statistical analysis appearing in 90% of the articles. ROC analysis is the single most common advanced technique. Authors make increasingly use of statistical experts' opinion and programs. During the last decade, the use of statistical methods in German radiological journals has fundamentally improved, both quantitatively and qualitatively. Presently, advanced techniques account for 20% of the pertinent statistical tests. This development seems to be promoted by the increasing availability of statistical analysis software.

Nateglinide versus repaglinide for type 2 diabetes mellitus in China.

PubMed

Li, Chanjuan; Xia, Jielai; Zhang, Gaokui; Wang, Suzhen; Wang, Ling

2009-12-01

The purpose of this study is to evaluate efficacy and safety of nateglinide tablet administration in comparison with those of repaglinide tablet as control on treating type 2 diabetes mellitus in China. Pooled-analysis with analysis of covariance (ANCOVA) method was applied to assess the efficacy and safety based on original data collected from four independent randomized clinical trials with similar research protocols. However meta-analysis was applied based on the outcomes of the four studies. The results by meta-analysis were comparable to those obtained by pooled-analysis. The means of HbA(1c), and fasting blood glucose in both the nateglinide and repaglinide groups were reduced significantly after 12 weeks duration but no statistical differences in reduction between the two groups. The adverse reaction rates were 9.89 and 6.51% in the nateglinide and repaglinide groups respectively, with the rate difference showing no statistical significance, and the Odds Ratio of adverse reaction rate (95% confidence interval) was 1.59 (0.99, 2.55). Both nateglinide and repaglinide administration have similarly significant effects on reducing HbA(1c) and FBG. However, the adverse reaction rate in the nateglinide group is higher than that in the latter using repaglinide but no statistical significance difference as revealed in the four clinical trials detailed below.

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

Analyzing Dyadic Sequence Data—Research Questions and Implied Statistical Models

PubMed Central

Fuchs, Peter; Nussbeck, Fridtjof W.; Meuwly, Nathalie; Bodenmann, Guy

2017-01-01

The analysis of observational data is often seen as a key approach to understanding dynamics in romantic relationships but also in dyadic systems in general. Statistical models for the analysis of dyadic observational data are not commonly known or applied. In this contribution, selected approaches to dyadic sequence data will be presented with a focus on models that can be applied when sample sizes are of medium size (N = 100 couples or less). Each of the statistical models is motivated by an underlying potential research question, the most important model results are presented and linked to the research question. The following research questions and models are compared with respect to their applicability using a hands on approach: (I) Is there an association between a particular behavior by one and the reaction by the other partner? (Pearson Correlation); (II) Does the behavior of one member trigger an immediate reaction by the other? (aggregated logit models; multi-level approach; basic Markov model); (III) Is there an underlying dyadic process, which might account for the observed behavior? (hidden Markov model); and (IV) Are there latent groups of dyads, which might account for observing different reaction patterns? (mixture Markov; optimal matching). Finally, recommendations for researchers to choose among the different models, issues of data handling, and advises to apply the statistical models in empirical research properly are given (e.g., in a new r-package “DySeq”). PMID:28443037

Geostatistics - a tool applied to the distribution of Legionella pneumophila in a hospital water system.

PubMed

Laganà, Pasqualina; Moscato, Umberto; Poscia, Andrea; La Milia, Daniele Ignazio; Boccia, Stefania; Avventuroso, Emanuela; Delia, Santi

2015-01-01

Legionnaires' disease is normally acquired by inhalation of legionellae from a contaminated environmental source. Water systems of large buildings, such as hospitals, are often contaminated with legionellae and therefore represent a potential risk for the hospital population. The aim of this study was to evaluate the potential contamination of Legionella pneumophila (LP) in a large hospital in Italy through georeferential statistical analysis to assess the possible sources of dispersion and, consequently, the risk of exposure for both health care staff and patients. LP serogroups 1 and 2-14 distribution was considered in the wards housed on two consecutive floors of the hospital building. On the basis of information provided by 53 bacteriological analysis, a 'random' grid of points was chosen and spatial geostatistics or FAIk Kriging was applied and compared with the results of classical statistical analysis. Over 50% of the examined samples were positive for Legionella pneumophila. LP 1 was isolated in 69% of samples from the ground floor and in 60% of sample from the first floor; LP 2-14 in 36% of sample from the ground floor and 24% from the first. The iso-estimation maps show clearly the most contaminated pipe and the difference in the diffusion of the different L. pneumophila serogroups. Experimental work has demonstrated that geostatistical methods applied to the microbiological analysis of water matrices allows a better modeling of the phenomenon under study, a greater potential for risk management and a greater choice of methods of prevention and environmental recovery to be put in place with respect to the classical statistical analysis.

Materials Approach to Dissecting Surface Responses in the Attachment Stages of Biofouling Organisms

DTIC Science & Technology

2016-04-25

their settlement behavior in regards to the coating surfaces. 5) Multivariate statistical analysis was used to examine the effect (if any) of the...applied to glass rods and were deployed in the field to evaluate settlement preferences. Canonical Analysis of Principal Coordinates were applied to...the influence of coating surface properties on the patterns in settlement observed in the field in the extension of this work over the coming year

Applying Bayesian statistics to the study of psychological trauma: A suggestion for future research.

PubMed

Yalch, Matthew M

2016-03-01

Several contemporary researchers have noted the virtues of Bayesian methods of data analysis. Although debates continue about whether conventional or Bayesian statistics is the "better" approach for researchers in general, there are reasons why Bayesian methods may be well suited to the study of psychological trauma in particular. This article describes how Bayesian statistics offers practical solutions to the problems of data non-normality, small sample size, and missing data common in research on psychological trauma. After a discussion of these problems and the effects they have on trauma research, this article explains the basic philosophical and statistical foundations of Bayesian statistics and how it provides solutions to these problems using an applied example. Results of the literature review and the accompanying example indicates the utility of Bayesian statistics in addressing problems common in trauma research. Bayesian statistics provides a set of methodological tools and a broader philosophical framework that is useful for trauma researchers. Methodological resources are also provided so that interested readers can learn more. (c) 2016 APA, all rights reserved).

Sedimentological analysis and bed thickness statistics from a Carboniferous deep-water channel-levee complex: Myall Trough, SE Australia

NASA Astrophysics Data System (ADS)

Palozzi, Jason; Pantopoulos, George; Maravelis, Angelos G.; Nordsvan, Adam; Zelilidis, Avraam

2018-02-01

This investigation presents an outcrop-based integrated study of internal division analysis and statistical treatment of turbidite bed thickness applied to a Carboniferous deep-water channel-levee complex in the Myall Trough, southeast Australia. Turbidite beds of the studied succession are characterized by a range of sedimentary structures grouped into two main associations, a thick-bedded and a thin-bedded one, that reflect channel-fill and overbank/levee deposits, respectively. Three vertically stacked channel-levee cycles have been identified. Results of statistical analysis of bed thickness, grain-size and internal division patterns applied on the studied channel-levee succession, indicate that turbidite bed thickness data seem to be well characterized by a bimodal lognormal distribution, which is possibly reflecting the difference between deposition from lower-density flows (in a levee/overbank setting) and very high-density flows (in a channel fill setting). Power law and exponential distributions were observed to hold only for the thick-bedded parts of the succession and cannot characterize the whole bed thickness range of the studied sediments. The succession also exhibits non-random clustering of bed thickness and grain-size measurements. The studied sediments are also characterized by the presence of statistically detected fining-upward sandstone packets. A novel quantitative approach (change-point analysis) is proposed for the detection of those packets. Markov permutation statistics also revealed the existence of order in the alternation of internal divisions in the succession expressed by an optimal internal division cycle reflecting two main types of gravity flow events deposited within both thick-bedded conglomeratic and thin-bedded sandstone associations. The analytical methods presented in this study can be used as additional tools for quantitative analysis and recognition of depositional environments in hydrocarbon-bearing research of ancient deep-water channel-levee settings.

Online Statistical Modeling (Regression Analysis) for Independent Responses

NASA Astrophysics Data System (ADS)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

How Does One Assess the Accuracy of Academic Success Predictors? ROC Analysis Applied to University Entrance Factors

ERIC Educational Resources Information Center

Vivo, Juana-Maria; Franco, Manuel

2008-01-01

This article attempts to present a novel application of a method of measuring accuracy for academic success predictors that could be used as a standard. This procedure is known as the receiver operating characteristic (ROC) curve, which comes from statistical decision techniques. The statistical prediction techniques provide predictor models and…

Asking Sensitive Questions: A Statistical Power Analysis of Randomized Response Models

ERIC Educational Resources Information Center

Ulrich, Rolf; Schroter, Hannes; Striegel, Heiko; Simon, Perikles

2012-01-01

This article derives the power curves for a Wald test that can be applied to randomized response models when small prevalence rates must be assessed (e.g., detecting doping behavior among elite athletes). These curves enable the assessment of the statistical power that is associated with each model (e.g., Warner's model, crosswise model, unrelated…

A Computer Evolution in Teaching Undergraduate Time Series

ERIC Educational Resources Information Center

Hodgess, Erin M.

2004-01-01

In teaching undergraduate time series courses, we have used a mixture of various statistical packages. We have finally been able to teach all of the applied concepts within one statistical package; R. This article describes the process that we use to conduct a thorough analysis of a time series. An example with a data set is provided. We compare…

A glossary for big data in population and public health: discussion and commentary on terminology and research methods.

PubMed

Fuller, Daniel; Buote, Richard; Stanley, Kevin

2017-11-01

The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Statistical Analysis of CFD Solutions From the Fifth AIAA Drag Prediction Workshop

NASA Technical Reports Server (NTRS)

Morrison, Joseph H.

2013-01-01

A graphical framework is used for statistical analysis of the results from an extensive N-version test of a collection of Reynolds-averaged Navier-Stokes computational fluid dynamics codes. The solutions were obtained by code developers and users from North America, Europe, Asia, and South America using a common grid sequence and multiple turbulence models for the June 2012 fifth Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration for this workshop was the Common Research Model subsonic transport wing-body previously used for the 4th Drag Prediction Workshop. This work continues the statistical analysis begun in the earlier workshops and compares the results from the grid convergence study of the most recent workshop with previous workshops.

Multi-Scale Surface Descriptors

PubMed Central

Cipriano, Gregory; Phillips, George N.; Gleicher, Michael

2010-01-01

Local shape descriptors compactly characterize regions of a surface, and have been applied to tasks in visualization, shape matching, and analysis. Classically, curvature has be used as a shape descriptor; however, this differential property characterizes only an infinitesimal neighborhood. In this paper, we provide shape descriptors for surface meshes designed to be multi-scale, that is, capable of characterizing regions of varying size. These descriptors capture statistically the shape of a neighborhood around a central point by fitting a quadratic surface. They therefore mimic differential curvature, are efficient to compute, and encode anisotropy. We show how simple variants of mesh operations can be used to compute the descriptors without resorting to expensive parameterizations, and additionally provide a statistical approximation for reduced computational cost. We show how these descriptors apply to a number of uses in visualization, analysis, and matching of surfaces, particularly to tasks in protein surface analysis. PMID:19834190

A phylogenetic transform enhances analysis of compositional microbiota data

PubMed Central

Silverman, Justin D; Washburne, Alex D; Mukherjee, Sayan; David, Lawrence A

2017-01-01

Surveys of microbial communities (microbiota), typically measured as relative abundance of species, have illustrated the importance of these communities in human health and disease. Yet, statistical artifacts commonly plague the analysis of relative abundance data. Here, we introduce the PhILR transform, which incorporates microbial evolutionary models with the isometric log-ratio transform to allow off-the-shelf statistical tools to be safely applied to microbiota surveys. We demonstrate that analyses of community-level structure can be applied to PhILR transformed data with performance on benchmarks rivaling or surpassing standard tools. Additionally, by decomposing distance in the PhILR transformed space, we identified neighboring clades that may have adapted to distinct human body sites. Decomposing variance revealed that covariation of bacterial clades within human body sites increases with phylogenetic relatedness. Together, these findings illustrate how the PhILR transform combines statistical and phylogenetic models to overcome compositional data challenges and enable evolutionary insights relevant to microbial communities. DOI: http://dx.doi.org/10.7554/eLife.21887.001 PMID:28198697

Scaling up to address data science challenges

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

Wendelberger, Joanne R.

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Scaling up to address data science challenges

DOE PAGES

Wendelberger, Joanne R.

2017-04-27

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Akterations/corrections to the BRASS Program

NASA Technical Reports Server (NTRS)

Brand, S. N.

1985-01-01

Corrections applied to statistical programs contained in two subroutines of the Bed Rest Analysis Software System (BRASS) are summarized. Two subroutines independently calculate significant values within the BRASS program.

New Statistics for Testing Differential Expression of Pathways from Microarray Data

NASA Astrophysics Data System (ADS)

Siu, Hoicheong; Dong, Hua; Jin, Li; Xiong, Momiao

Exploring biological meaning from microarray data is very important but remains a great challenge. Here, we developed three new statistics: linear combination test, quadratic test and de-correlation test to identify differentially expressed pathways from gene expression profile. We apply our statistics to two rheumatoid arthritis datasets. Notably, our results reveal three significant pathways and 275 genes in common in two datasets. The pathways we found are meaningful to uncover the disease mechanisms of rheumatoid arthritis, which implies that our statistics are a powerful tool in functional analysis of gene expression data.

An Analysis of COLA (Cost of Living Adjustment) Allocation within the United States Coast Guard.

DTIC Science & Technology

1983-09-01

books Applied Linear Regression [Ref. 39], and Statistical Methods in Research and Production [Ref. 40], or any other book on regression. In the event...Indexes, Master’s Thesis, Air Force Institute of Technology, Wright-Patterson AFB, 1976. 39. Weisberg, Stanford, Applied Linear Regression , Wiley, 1980. 40

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

NASA Astrophysics Data System (ADS)

Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface waters can be undertaken.

Spatio-temporal analysis of annual rainfall in Crete, Greece

NASA Astrophysics Data System (ADS)

Varouchakis, Emmanouil A.; Corzo, Gerald A.; Karatzas, George P.; Kotsopoulou, Anastasia

2018-03-01

Analysis of rainfall data from the island of Crete, Greece was performed to identify key hydrological years and return periods as well as to analyze the inter-annual behavior of the rainfall variability during the period 1981-2014. The rainfall spatial distribution was also examined in detail to identify vulnerable areas of the island. Data analysis using statistical tools and spectral analysis were applied to investigate and interpret the temporal course of the available rainfall data set. In addition, spatial analysis techniques were applied and compared to determine the rainfall spatial distribution on the island of Crete. The analysis presented that in contrast to Regional Climate Model estimations, rainfall rates have not decreased, while return periods vary depending on seasonality and geographic location. A small but statistical significant increasing trend was detected in the inter-annual rainfall variations as well as a significant rainfall cycle almost every 8 years. In addition, statistically significant correlation of the island's rainfall variability with the North Atlantic Oscillation is identified for the examined period. On the other hand, regression kriging method combining surface elevation as secondary information improved the estimation of the annual rainfall spatial variability on the island of Crete by 70% compared to ordinary kriging. The rainfall spatial and temporal trends on the island of Crete have variable characteristics that depend on the geographical area and on the hydrological period.

Multivariate Statistical Analysis: a tool for groundwater quality assessment in the hidrogeologic region of the Ring of Cenotes, Yucatan, Mexico.

NASA Astrophysics Data System (ADS)

Ye, M.; Pacheco Castro, R. B.; Pacheco Avila, J.; Cabrera Sansores, A.

2014-12-01

The karstic aquifer of Yucatan is a vulnerable and complex system. The first fifteen meters of this aquifer have been polluted, due to this the protection of this resource is important because is the only source of potable water of the entire State. Through the assessment of groundwater quality we can gain some knowledge about the main processes governing water chemistry as well as spatial patterns which are important to establish protection zones. In this work multivariate statistical techniques are used to assess the groundwater quality of the supply wells (30 to 40 meters deep) in the hidrogeologic region of the Ring of Cenotes, located in Yucatan, Mexico. Cluster analysis and principal component analysis are applied in groundwater chemistry data of the study area. Results of principal component analysis show that the main sources of variation in the data are due sea water intrusion and the interaction of the water with the carbonate rocks of the system and some pollution processes. The cluster analysis shows that the data can be divided in four clusters. The spatial distribution of the clusters seems to be random, but is consistent with sea water intrusion and pollution with nitrates. The overall results show that multivariate statistical analysis can be successfully applied in the groundwater quality assessment of this karstic aquifer.

A Survey of Popular R Packages for Cluster Analysis

ERIC Educational Resources Information Center

Flynt, Abby; Dean, Nema

2016-01-01

Cluster analysis is a set of statistical methods for discovering new group/class structure when exploring data sets. This article reviews the following popular libraries/commands in the R software language for applying different types of cluster analysis: from the stats library, the kmeans, and hclust functions; the mclust library; the poLCA…

Graph theory applied to noise and vibration control in statistical energy analysis models.

PubMed

Guasch, Oriol; Cortés, Lluís

2009-06-01

A fundamental aspect of noise and vibration control in statistical energy analysis (SEA) models consists in first identifying and then reducing the energy flow paths between subsystems. In this work, it is proposed to make use of some results from graph theory to address both issues. On the one hand, linear and path algebras applied to adjacency matrices of SEA graphs are used to determine the existence of any order paths between subsystems, counting and labeling them, finding extremal paths, or determining the power flow contributions from groups of paths. On the other hand, a strategy is presented that makes use of graph cut algorithms to reduce the energy flow from a source subsystem to a receiver one, modifying as few internal and coupling loss factors as possible.

Phantom Effects in Multilevel Compositional Analysis: Problems and Solutions

ERIC Educational Resources Information Center

Pokropek, Artur

2015-01-01

This article combines statistical and applied research perspective showing problems that might arise when measurement error in multilevel compositional effects analysis is ignored. This article focuses on data where independent variables are constructed measures. Simulation studies are conducted evaluating methods that could overcome the…

Analysis of wind bias change with respect to time at Cape Kennedy, Florida, and Vandenberg AFB, California

NASA Technical Reports Server (NTRS)

Adelfang, S. I.

1978-01-01

A statistical analysis is presented of the temporal variability of wind vectors at 1 km altitude intervals from 0 to 27 km altitude after applying a digital filter to the original wind profile data sample.

Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing.

PubMed

Willems, Sander; Fraiture, Marie-Alice; Deforce, Dieter; De Keersmaecker, Sigrid C J; De Loose, Marc; Ruttink, Tom; Herman, Philippe; Van Nieuwerburgh, Filip; Roosens, Nancy

2016-02-01

Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgene event in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Divergence Statistics Extension to VTK for Performance Analysis

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

Pebay, Philippe Pierre; Bennett, Janine Camille

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,more » "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.« less

Statistical analysis of sparse infection data and its implications for retroviral treatment trials in primates.

PubMed Central

Spouge, J L

1992-01-01

Reports on retroviral primate trials rarely publish any statistical analysis. Present statistical methodology lacks appropriate tests for these trials and effectively discourages quantitative assessment. This paper describes the theory behind VACMAN, a user-friendly computer program that calculates statistics for in vitro and in vivo infectivity data. VACMAN's analysis applies to many retroviral trials using i.v. challenges and is valid whenever the viral dose-response curve has a particular shape. Statistics from actual i.v. retroviral trials illustrate some unappreciated principles of effective animal use: dilutions other than 1:10 can improve titration accuracy; infecting titration animals at the lowest doses possible can lower challenge doses; and finally, challenging test animals in small trials with more virus than controls safeguards against false successes, "reuses" animals, and strengthens experimental conclusions. The theory presented also explains the important concept of viral saturation, a phenomenon that may cause in vitro and in vivo titrations to agree for some retroviral strains and disagree for others. PMID:1323844

Quantitation of flavonoid constituents in citrus fruits.

PubMed

Kawaii, S; Tomono, Y; Katase, E; Ogawa, K; Yano, M

1999-09-01

Twenty-four flavonoids have been determined in 66 Citrus species and near-citrus relatives, grown in the same field and year, by means of reversed phase high-performance liquid chromatography analysis. Statistical methods have been applied to find relations among the species. The F ratios of 21 flavonoids obtained by applying ANOVA analysis are significant, indicating that a classification of the species using these variables is reasonable to pursue. Principal component analysis revealed that the distributions of Citrus species belonging to different classes were largely in accordance with Tanaka's classification system.

Regional Morphology Analysis Package (RMAP): Empirical Orthogonal Function Analysis, Background and Examples

DTIC Science & Technology

2007-10-01

1984. Complex principal component analysis : Theory and examples. Journal of Climate and Applied Meteorology 23: 1660-1673. Hotelling, H. 1933...Sediments 99. ASCE: 2,566-2,581. Von Storch, H., and A. Navarra. 1995. Analysis of climate variability. Applications of statistical techniques. Berlin...ERDC TN-SWWRP-07-9 October 2007 Regional Morphology Empirical Analysis Package (RMAP): Orthogonal Function Analysis , Background and Examples by

[The evaluation of costs: standards of medical care and clinical statistic groups].

PubMed

Semenov, V Iu; Samorodskaia, I V

2014-01-01

The article presents the comparative analysis of techniques of evaluation of costs of hospital treatment using medical economic standards of medical care and clinical statistical groups. The technique of evaluation of costs on the basis of clinical statistical groups was developed almost fifty years ago and is largely applied in a number of countries. Nowadays, in Russia the payment for completed case of treatment on the basis of medical economic standards is the main mode of payment for medical care in hospital. It is very conditionally a Russian analogue of world-wide prevalent system of diagnostic related groups. The tariffs for these cases of treatment as opposed to clinical statistical groups are counted on basis of standards of provision of medical care approved by Minzdrav of Russia. The information derived from generalization of cases of treatment of real patients is not applied.

Bayesian models based on test statistics for multiple hypothesis testing problems.

PubMed

Ji, Yuan; Lu, Yiling; Mills, Gordon B

2008-04-01

We propose a Bayesian method for the problem of multiple hypothesis testing that is routinely encountered in bioinformatics research, such as the differential gene expression analysis. Our algorithm is based on modeling the distributions of test statistics under both null and alternative hypotheses. We substantially reduce the complexity of the process of defining posterior model probabilities by modeling the test statistics directly instead of modeling the full data. Computationally, we apply a Bayesian FDR approach to control the number of rejections of null hypotheses. To check if our model assumptions for the test statistics are valid for various bioinformatics experiments, we also propose a simple graphical model-assessment tool. Using extensive simulations, we demonstrate the performance of our models and the utility of the model-assessment tool. In the end, we apply the proposed methodology to an siRNA screening and a gene expression experiment.

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

PubMed Central

Lee, Seunggeun; Teslovich, Tanya M.; Boehnke, Michael; Lin, Xihong

2013-01-01

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels. PMID:23768515

Event coincidence analysis for quantifying statistical interrelationships between event time series. On the role of flood events as triggers of epidemic outbreaks

NASA Astrophysics Data System (ADS)

Donges, J. F.; Schleussner, C.-F.; Siegmund, J. F.; Donner, R. V.

2016-05-01

Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Applying the framework to country-level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.

Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique

NASA Astrophysics Data System (ADS)

Arif, Sajjad; Tanwir Alam, Md; Ansari, Akhter H.; Bilal Naim Shaikh, Mohd; Arif Siddiqui, M.

2018-05-01

The tribological performance of aluminium hybrid composites reinforced with micro SiC (5 wt%) and nano zirconia (0, 3, 6 and 9 wt%) fabricated through powder metallurgy technique were investigated using statistical and artificial neural network (ANN) approach. The influence of zirconia reinforcement, sliding distance and applied load were analyzed with test based on full factorial design of experiments. Analysis of variance (ANOVA) was used to evaluate the percentage contribution of each process parameters on wear loss. ANOVA approach suggested that wear loss be mainly influenced by sliding distance followed by zirconia reinforcement and applied load. Further, a feed forward back propagation neural network was applied on input/output date for predicting and analyzing the wear behaviour of fabricated composite. A very close correlation between experimental and ANN output were achieved by implementing the model. Finally, ANN model was effectively used to find the influence of various control factors on wear behaviour of hybrid composites.

Upside/Downside statistical mechanics of nonequilibrium Brownian motion. I. Distributions, moments, and correlation functions of a free particle.

PubMed

Craven, Galen T; Nitzan, Abraham

2018-01-28

Statistical properties of Brownian motion that arise by analyzing, separately, trajectories over which the system energy increases (upside) or decreases (downside) with respect to a threshold energy level are derived. This selective analysis is applied to examine transport properties of a nonequilibrium Brownian process that is coupled to multiple thermal sources characterized by different temperatures. Distributions, moments, and correlation functions of a free particle that occur during upside and downside events are investigated for energy activation and energy relaxation processes and also for positive and negative energy fluctuations from the average energy. The presented results are sufficiently general and can be applied without modification to the standard Brownian motion. This article focuses on the mathematical basis of this selective analysis. In subsequent articles in this series, we apply this general formalism to processes in which heat transfer between thermal reservoirs is mediated by activated rate processes that take place in a system bridging them.

Upside/Downside statistical mechanics of nonequilibrium Brownian motion. I. Distributions, moments, and correlation functions of a free particle

NASA Astrophysics Data System (ADS)

Craven, Galen T.; Nitzan, Abraham

2018-01-01

Statistical properties of Brownian motion that arise by analyzing, separately, trajectories over which the system energy increases (upside) or decreases (downside) with respect to a threshold energy level are derived. This selective analysis is applied to examine transport properties of a nonequilibrium Brownian process that is coupled to multiple thermal sources characterized by different temperatures. Distributions, moments, and correlation functions of a free particle that occur during upside and downside events are investigated for energy activation and energy relaxation processes and also for positive and negative energy fluctuations from the average energy. The presented results are sufficiently general and can be applied without modification to the standard Brownian motion. This article focuses on the mathematical basis of this selective analysis. In subsequent articles in this series, we apply this general formalism to processes in which heat transfer between thermal reservoirs is mediated by activated rate processes that take place in a system bridging them.

On the Use of Statistics in Design and the Implications for Deterministic Computer Experiments

NASA Technical Reports Server (NTRS)

Simpson, Timothy W.; Peplinski, Jesse; Koch, Patrick N.; Allen, Janet K.

1997-01-01

Perhaps the most prevalent use of statistics in engineering design is through Taguchi's parameter and robust design -- using orthogonal arrays to compute signal-to-noise ratios in a process of design improvement. In our view, however, there is an equally exciting use of statistics in design that could become just as prevalent: it is the concept of metamodeling whereby statistical models are built to approximate detailed computer analysis codes. Although computers continue to get faster, analysis codes always seem to keep pace so that their computational time remains non-trivial. Through metamodeling, approximations of these codes are built that are orders of magnitude cheaper to run. These metamodels can then be linked to optimization routines for fast analysis, or they can serve as a bridge for integrating analysis codes across different domains. In this paper we first review metamodeling techniques that encompass design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We discuss their existing applications in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of metamodeling techniques in given situations and how common pitfalls can be avoided.

Statistical dependency in visual scanning

NASA Technical Reports Server (NTRS)

Ellis, Stephen R.; Stark, Lawrence

1986-01-01

A method to identify statistical dependencies in the positions of eye fixations is developed and applied to eye movement data from subjects who viewed dynamic displays of air traffic and judged future relative position of aircraft. Analysis of approximately 23,000 fixations on points of interest on the display identified statistical dependencies in scanning that were independent of the physical placement of the points of interest. Identification of these dependencies is inconsistent with random-sampling-based theories used to model visual search and information seeking.

The l z ( p ) * Person-Fit Statistic in an Unfolding Model Context.

PubMed

Tendeiro, Jorge N

2017-01-01

Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded unfolding model is used. Results from a simulation study indicate that the person-fit statistic performed relatively well in detecting midpoint response style patterns and not so well in detecting extreme response style patterns.

Visualization of the variability of 3D statistical shape models by animation.

PubMed

Lamecker, Hans; Seebass, Martin; Lange, Thomas; Hege, Hans-Christian; Deuflhard, Peter

2004-01-01

Models of the 3D shape of anatomical objects and the knowledge about their statistical variability are of great benefit in many computer assisted medical applications like images analysis, therapy or surgery planning. Statistical model of shapes have successfully been applied to automate the task of image segmentation. The generation of 3D statistical shape models requires the identification of corresponding points on two shapes. This remains a difficult problem, especially for shapes of complicated topology. In order to interpret and validate variations encoded in a statistical shape model, visual inspection is of great importance. This work describes the generation and interpretation of statistical shape models of the liver and the pelvic bone.

Assessment of Coastal and Urban Flooding Hazards Applying Extreme Value Analysis and Multivariate Statistical Techniques: A Case Study in Elwood, Australia

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik

2016-04-01

Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.

Mathematics and statistics research progress report, period ending June 30, 1983

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

Beauchamp, J. J.; Denson, M. V.; Heath, M. T.

1983-08-01

This report is the twenty-sixth in the series of progress reports of Mathematics and Statistics Research of the Computer Sciences organization, Union Carbide Corporation Nuclear Division. Part A records research progress in analysis of large data sets, applied analysis, biometrics research, computational statistics, materials science applications, numerical linear algebra, and risk analysis. Collaboration and consulting with others throughout the Oak Ridge Department of Energy complex are recorded in Part B. Included are sections on biological sciences, energy, engineering, environmental sciences, health and safety, and safeguards. Part C summarizes the various educational activities in which the staff was engaged. Part Dmore » lists the presentations of research results, and Part E records the staff's other professional activities during the report period.« less

Methods for trend analysis: Examples with problem/failure data

NASA Technical Reports Server (NTRS)

Church, Curtis K.

1989-01-01

Statistics are emphasized as an important role in quality control and reliability. Consequently, Trend Analysis Techniques recommended a variety of statistical methodologies that could be applied to time series data. The major goal of the working handbook, using data from the MSFC Problem Assessment System, is to illustrate some of the techniques in the NASA standard, some different techniques, and to notice patterns of data. Techniques for trend estimation used are: regression (exponential, power, reciprocal, straight line) and Kendall's rank correlation coefficient. The important details of a statistical strategy for estimating a trend component are covered in the examples. However, careful analysis and interpretation is necessary because of small samples and frequent zero problem reports in a given time period. Further investigations to deal with these issues are being conducted.

Microscopic saw mark analysis: an empirical approach.

PubMed

Love, Jennifer C; Derrick, Sharon M; Wiersema, Jason M; Peters, Charles

2015-01-01

Microscopic saw mark analysis is a well published and generally accepted qualitative analytical method. However, little research has focused on identifying and mitigating potential sources of error associated with the method. The presented study proposes the use of classification trees and random forest classifiers as an optimal, statistically sound approach to mitigate the potential for error of variability and outcome error in microscopic saw mark analysis. The statistical model was applied to 58 experimental saw marks created with four types of saws. The saw marks were made in fresh human femurs obtained through anatomical gift and were analyzed using a Keyence digital microscope. The statistical approach weighed the variables based on discriminatory value and produced decision trees with an associated outcome error rate of 8.62-17.82%. © 2014 American Academy of Forensic Sciences.

A new strategy for statistical analysis-based fingerprint establishment: Application to quality assessment of Semen sojae praeparatum.

PubMed

Guo, Hui; Zhang, Zhen; Yao, Yuan; Liu, Jialin; Chang, Ruirui; Liu, Zhao; Hao, Hongyuan; Huang, Taohong; Wen, Jun; Zhou, Tingting

2018-08-30

Semen sojae praeparatum with homology of medicine and food is a famous traditional Chinese medicine. A simple and effective quality fingerprint analysis, coupled with chemometrics methods, was developed for quality assessment of Semen sojae praeparatum. First, similarity analysis (SA) and hierarchical clusting analysis (HCA) were applied to select the qualitative markers, which obviously influence the quality of Semen sojae praeparatum. 21 chemicals were selected and characterized by high resolution ion trap/time-of-flight mass spectrometry (LC-IT-TOF-MS). Subsequently, principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to select the quantitative markers of Semen sojae praeparatum samples from different origins. Moreover, 11 compounds with statistical significance were determined quantitatively, which provided an accurate and informative data for quality evaluation. This study proposes a new strategy for "statistic analysis-based fingerprint establishment", which would be a valuable reference for further study. Copyright © 2018 Elsevier Ltd. All rights reserved.

[The principal components analysis--method to classify the statistical variables with applications in medicine].

PubMed

Dascălu, Cristina Gena; Antohe, Magda Ecaterina

2009-01-01

Based on the eigenvalues and the eigenvectors analysis, the principal component analysis has the purpose to identify the subspace of the main components from a set of parameters, which are enough to characterize the whole set of parameters. Interpreting the data for analysis as a cloud of points, we find through geometrical transformations the directions where the cloud's dispersion is maximal--the lines that pass through the cloud's center of weight and have a maximal density of points around them (by defining an appropriate criteria function and its minimization. This method can be successfully used in order to simplify the statistical analysis on questionnaires--because it helps us to select from a set of items only the most relevant ones, which cover the variations of the whole set of data. For instance, in the presented sample we started from a questionnaire with 28 items and, applying the principal component analysis we identified 7 principal components--or main items--fact that simplifies significantly the further data statistical analysis.

Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data.

PubMed

Rhodes, Kirsty M; Turner, Rebecca M; White, Ian R; Jackson, Dan; Spiegelhalter, David J; Higgins, Julian P T

2016-12-20

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Regression: The Apple Does Not Fall Far From the Tree.

PubMed

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

SU-E-J-261: Statistical Analysis and Chaotic Dynamics of Respiratory Signal of Patients in BodyFix

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

Michalski, D; Huq, M; Bednarz, G

Purpose: To quantify respiratory signal of patients in BodyFix undergoing 4DCT scan with and without immobilization cover. Methods: 20 pairs of respiratory tracks recorded with RPM system during 4DCT scan were analyzed. Descriptive statistic was applied to selected parameters of exhale-inhale decomposition. Standardized signals were used with the delay method to build orbits in embedded space. Nonlinear behavior was tested with surrogate data. Sample entropy SE, Lempel-Ziv complexity LZC and the largest Lyapunov exponents LLE were compared. Results: Statistical tests show difference between scans for inspiration time and its variability, which is bigger for scans without cover. The same ismore » for variability of the end of exhalation and inhalation. Other parameters fail to show the difference. For both scans respiratory signals show determinism and nonlinear stationarity. Statistical test on surrogate data reveals their nonlinearity. LLEs show signals chaotic nature and its correlation with breathing period and its embedding delay time. SE, LZC and LLE measure respiratory signal complexity. Nonlinear characteristics do not differ between scans. Conclusion: Contrary to expectation cover applied to patients in BodyFix appears to have limited effect on signal parameters. Analysis based on trajectories of delay vectors shows respiratory system nonlinear character and its sensitive dependence on initial conditions. Reproducibility of respiratory signal can be evaluated with measures of signal complexity and its predictability window. Longer respiratory period is conducive for signal reproducibility as shown by these gauges. Statistical independence of the exhale and inhale times is also supported by the magnitude of LLE. The nonlinear parameters seem more appropriate to gauge respiratory signal complexity since its deterministic chaotic nature. It contrasts with measures based on harmonic analysis that are blind for nonlinear features. Dynamics of breathing, so crucial for 4D-based clinical technologies, can be better controlled if nonlinear-based methodology, which reflects respiration characteristic, is applied. Funding provided by Varian Medical Systems via Investigator Initiated Research Project.« less

The vulnerability of electric equipment to carbon fibers of mixed lengths: An analysis

NASA Technical Reports Server (NTRS)

Elber, W.

1980-01-01

The susceptibility of a stereo amplifier to damage from a spectrum of lengths of graphite fibers was calculated. A simple analysis was developed by which such calculations can be based on test results with fibers of uniform lengths. A statistical analysis was applied for the conversation of data for various logical failure criteria.

[Statistical analysis of body and lung mass of animals subjected to a single experimental insufflation of soil dust and electro-energetic ashes].

PubMed

Matysiak, W; Królikowska-Prasał, I; Staszyc, J; Kifer, E; Romanowska-Sarlej, J

1989-01-01

The studies were performed on 44 white female Wistar rats which were intratracheally administered the suspension of the soil dust and the electro-energetic ashes. The electro-energetic ashes were collected from 6 different local heat and power generating plants while the soil dust from several random places of our country. The statistical analysis of the body and the lung mass of the animals subjected to the single dust and ash insufflation was performed. The applied variants proved the statistically significant differences between the body and the lung mass. The observed differences are connected with the kinds of dust and ash used in the experiment.

Energy-density field approach for low- and medium-frequency vibroacoustic analysis of complex structures using a statistical computational model

NASA Astrophysics Data System (ADS)

Kassem, M.; Soize, C.; Gagliardini, L.

2009-06-01

In this paper, an energy-density field approach applied to the vibroacoustic analysis of complex industrial structures in the low- and medium-frequency ranges is presented. This approach uses a statistical computational model. The analyzed system consists of an automotive vehicle structure coupled with its internal acoustic cavity. The objective of this paper is to make use of the statistical properties of the frequency response functions of the vibroacoustic system observed from previous experimental and numerical work. The frequency response functions are expressed in terms of a dimensionless matrix which is estimated using the proposed energy approach. Using this dimensionless matrix, a simplified vibroacoustic model is proposed.

Bayesian approach for counting experiment statistics applied to a neutrino point source analysis

NASA Astrophysics Data System (ADS)

Bose, D.; Brayeur, L.; Casier, M.; de Vries, K. D.; Golup, G.; van Eijndhoven, N.

2013-12-01

In this paper we present a model independent analysis method following Bayesian statistics to analyse data from a generic counting experiment and apply it to the search for neutrinos from point sources. We discuss a test statistic defined following a Bayesian framework that will be used in the search for a signal. In case no signal is found, we derive an upper limit without the introduction of approximations. The Bayesian approach allows us to obtain the full probability density function for both the background and the signal rate. As such, we have direct access to any signal upper limit. The upper limit derivation directly compares with a frequentist approach and is robust in the case of low-counting observations. Furthermore, it allows also to account for previous upper limits obtained by other analyses via the concept of prior information without the need of the ad hoc application of trial factors. To investigate the validity of the presented Bayesian approach, we have applied this method to the public IceCube 40-string configuration data for 10 nearby blazars and we have obtained a flux upper limit, which is in agreement with the upper limits determined via a frequentist approach. Furthermore, the upper limit obtained compares well with the previously published result of IceCube, using the same data set.

Determining Sample Sizes for Precise Contrast Analysis with Heterogeneous Variances

ERIC Educational Resources Information Center

Jan, Show-Li; Shieh, Gwowen

2014-01-01

The analysis of variance (ANOVA) is one of the most frequently used statistical analyses in practical applications. Accordingly, the single and multiple comparison procedures are frequently applied to assess the differences among mean effects. However, the underlying assumption of homogeneous variances may not always be tenable. This study…

RANDOMIZATION PROCEDURES FOR THE ANALYSIS OF EDUCATIONAL EXPERIMENTS.

ERIC Educational Resources Information Center

COLLIER, RAYMOND O.

CERTAIN SPECIFIC ASPECTS OF HYPOTHESIS TESTS USED FOR ANALYSIS OF RESULTS IN RANDOMIZED EXPERIMENTS WERE STUDIED--(1) THE DEVELOPMENT OF THE THEORETICAL FACTOR, THAT OF PROVIDING INFORMATION ON STATISTICAL TESTS FOR CERTAIN EXPERIMENTAL DESIGNS AND (2) THE DEVELOPMENT OF THE APPLIED ELEMENT, THAT OF SUPPLYING THE EXPERIMENTER WITH MACHINERY FOR…

Type Ia Supernova Intrinsic Magnitude Dispersion and the Fitting of Cosmological Parameters

NASA Astrophysics Data System (ADS)

Kim, A. G.

2011-02-01

I present an analysis for fitting cosmological parameters from a Hubble diagram of a standard candle with unknown intrinsic magnitude dispersion. The dispersion is determined from the data, simultaneously with the cosmological parameters. This contrasts with the strategies used to date. The advantages of the presented analysis are that it is done in a single fit (it is not iterative), it provides a statistically founded and unbiased estimate of the intrinsic dispersion, and its cosmological-parameter uncertainties account for the intrinsic-dispersion uncertainty. Applied to Type Ia supernovae, my strategy provides a statistical measure to test for subtypes and assess the significance of any magnitude corrections applied to the calibrated candle. Parameter bias and differences between likelihood distributions produced by the presented and currently used fitters are negligibly small for existing and projected supernova data sets.

Text mining factor analysis (TFA) in green tea patent data

NASA Astrophysics Data System (ADS)

Rahmawati, Sela; Suprijadi, Jadi; Zulhanif

2017-03-01

Factor analysis has become one of the most widely used multivariate statistical procedures in applied research endeavors across a multitude of domains. There are two main types of analyses based on factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Both EFA and CFA aim to observed relationships among a group of indicators with a latent variable, but they differ fundamentally, a priori and restrictions made to the factor model. This method will be applied to patent data technology sector green tea to determine the development technology of green tea in the world. Patent analysis is useful in identifying the future technological trends in a specific field of technology. Database patent are obtained from agency European Patent Organization (EPO). In this paper, CFA model will be applied to the nominal data, which obtain from the presence absence matrix. While doing processing, analysis CFA for nominal data analysis was based on Tetrachoric matrix. Meanwhile, EFA model will be applied on a title from sector technology dominant. Title will be pre-processing first using text mining analysis.

Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution

PubMed Central

Moretti, Stefano; van Leeuwen, Danitsja; Gmuender, Hans; Bonassi, Stefano; van Delft, Joost; Kleinjans, Jos; Patrone, Fioravante; Merlo, Domenico Franco

2008-01-01

Background In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. Results In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh), is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. Conclusion CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a synergistic effect between coalitional games and statistics that resulted in a selection of genes with a potential impact in the regulation of complex pathways. PMID:18764936

Cost-Effectiveness Analysis: a proposal of new reporting standards in statistical analysis

PubMed Central

Bang, Heejung; Zhao, Hongwei

2014-01-01

Cost-effectiveness analysis (CEA) is a method for evaluating the outcomes and costs of competing strategies designed to improve health, and has been applied to a variety of different scientific fields. Yet, there are inherent complexities in cost estimation and CEA from statistical perspectives (e.g., skewness, bi-dimensionality, and censoring). The incremental cost-effectiveness ratio that represents the additional cost per one unit of outcome gained by a new strategy has served as the most widely accepted methodology in the CEA. In this article, we call for expanded perspectives and reporting standards reflecting a more comprehensive analysis that can elucidate different aspects of available data. Specifically, we propose that mean and median-based incremental cost-effectiveness ratios and average cost-effectiveness ratios be reported together, along with relevant summary and inferential statistics as complementary measures for informed decision making. PMID:24605979

Statistical shape analysis using 3D Poisson equation--A quantitatively validated approach.

PubMed

Gao, Yi; Bouix, Sylvain

2016-05-01

Statistical shape analysis has been an important area of research with applications in biology, anatomy, neuroscience, agriculture, paleontology, etc. Unfortunately, the proposed methods are rarely quantitatively evaluated, and as shown in recent studies, when they are evaluated, significant discrepancies exist in their outputs. In this work, we concentrate on the problem of finding the consistent location of deformation between two population of shapes. We propose a new shape analysis algorithm along with a framework to perform a quantitative evaluation of its performance. Specifically, the algorithm constructs a Signed Poisson Map (SPoM) by solving two Poisson equations on the volumetric shapes of arbitrary topology, and statistical analysis is then carried out on the SPoMs. The method is quantitatively evaluated on synthetic shapes and applied on real shape data sets in brain structures. Copyright © 2016 Elsevier B.V. All rights reserved.

An issue of literacy on pediatric arterial hypertension

NASA Astrophysics Data System (ADS)

Teodoro, M. Filomena; Romana, Andreia; Simão, Carla

2017-11-01

Arterial hypertension in pediatric age is a public health problem, whose prevalence has increased significantly over time. Pediatric arterial hypertension (PAH) is under-diagnosed in most cases, a highly prevalent disease, appears without notice with multiple consequences on the children's health and future adults. Children caregivers and close family must know the PAH existence, the negative consequences associated with it, the risk factors and, finally, must do prevention. In [12, 13] can be found a statistical data analysis using a simpler questionnaire introduced in [4] under the aim of a preliminary study about PAH caregivers acquaintance. A continuation of such analysis is detailed in [14]. An extension of such questionnaire was built and applied to a distinct population and it was filled online. The statistical approach is partially reproduced in the present work. Some statistical models were estimated using several approaches, namely multivariate analysis (factorial analysis), also adequate methods to analyze the kind of data in study.

[Cluster analysis applicability to fitness evaluation of cosmonauts on long-term missions of the International space station].

PubMed

Egorov, A D; Stepantsov, V I; Nosovskiĭ, A M; Shipov, A A

2009-01-01

Cluster analysis was applied to evaluate locomotion training (running and running intermingled with walking) of 13 cosmonauts on long-term ISS missions by the parameters of duration (min), distance (m) and intensity (km/h). Based on the results of analyses, the cosmonauts were distributed into three steady groups of 2, 5 and 6 persons. Distance and speed showed a statistical rise (p < 0.03) from group 1 to group 3. Duration of physical locomotion training was not statistically different in the groups (p = 0.125). Therefore, cluster analysis is an adequate method of evaluating fitness of cosmonauts on long-term missions.

Applied statistical training to strengthen analysis and health research capacity in Rwanda.

PubMed

Thomson, Dana R; Semakula, Muhammed; Hirschhorn, Lisa R; Murray, Megan; Ndahindwa, Vedaste; Manzi, Anatole; Mukabutera, Assumpta; Karema, Corine; Condo, Jeanine; Hedt-Gauthier, Bethany

2016-09-29

To guide efficient investment of limited health resources in sub-Saharan Africa, local researchers need to be involved in, and guide, health system and policy research. While extensive survey and census data are available to health researchers and program officers in resource-limited countries, local involvement and leadership in research is limited due to inadequate experience, lack of dedicated research time and weak interagency connections, among other challenges. Many research-strengthening initiatives host prolonged fellowships out-of-country, yet their approaches have not been evaluated for effectiveness in involvement and development of local leadership in research. We developed, implemented and evaluated a multi-month, deliverable-driven, survey analysis training based in Rwanda to strengthen skills of five local research leaders, 15 statisticians, and a PhD candidate. Research leaders applied with a specific research question relevant to country challenges and committed to leading an analysis to publication. Statisticians with prerequisite statistical training and experience with a statistical software applied to participate in class-based trainings and complete an assigned analysis. Both statisticians and research leaders were provided ongoing in-country mentoring for analysis and manuscript writing. Participants reported a high level of skill, knowledge and collaborator development from class-based trainings and out-of-class mentorship that were sustained 1 year later. Five of six manuscripts were authored by multi-institution teams and submitted to international peer-reviewed scientific journals, and three-quarters of the participants mentored others in survey data analysis or conducted an additional survey analysis in the year following the training. Our model was effective in utilizing existing survey data and strengthening skills among full-time working professionals without disrupting ongoing work commitments and using few resources. Critical to our success were a transparent, robust application process and time limited training supplemented by ongoing, in-country mentoring toward manuscript deliverables that were led by Rwanda's health research leaders.

Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials.

PubMed

Hilgers, Ralf-Dieter; Bogdan, Malgorzata; Burman, Carl-Fredrik; Dette, Holger; Karlsson, Mats; König, Franz; Male, Christoph; Mentré, France; Molenberghs, Geert; Senn, Stephen

2018-05-11

IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers. The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project's more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages' output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials. The results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl's work as well as relating important methodologies by IDeAl's definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials. IDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.

A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data

PubMed Central

Zhu, Yun; Fan, Ruzong; Xiong, Momiao

2017-01-01

Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics. PMID:29040274

The Fusion of Financial Analysis and Seismology: Statistical Methods from Financial Market Analysis Applied to Earthquake Data

NASA Astrophysics Data System (ADS)

Ohyanagi, S.; Dileonardo, C.

2013-12-01

As a natural phenomenon earthquake occurrence is difficult to predict. Statistical analysis of earthquake data was performed using candlestick chart and Bollinger Band methods. These statistical methods, commonly used in the financial world to analyze market trends were tested against earthquake data. Earthquakes above Mw 4.0 located on shore of Sanriku (37.75°N ~ 41.00°N, 143.00°E ~ 144.50°E) from February 1973 to May 2013 were selected for analysis. Two specific patterns in earthquake occurrence were recognized through the analysis. One is a spread of candlestick prior to the occurrence of events greater than Mw 6.0. A second pattern shows convergence in the Bollinger Band, which implies a positive or negative change in the trend of earthquakes. Both patterns match general models for the buildup and release of strain through the earthquake cycle, and agree with both the characteristics of the candlestick chart and Bollinger Band analysis. These results show there is a high correlation between patterns in earthquake occurrence and trend analysis by these two statistical methods. The results of this study agree with the appropriateness of the application of these financial analysis methods to the analysis of earthquake occurrence.

Gene flow analysis method, the D-statistic, is robust in a wide parameter space.

PubMed

Zheng, Yichen; Janke, Axel

2018-01-08

We evaluated the sensitivity of the D-statistic, a parsimony-like method widely used to detect gene flow between closely related species. This method has been applied to a variety of taxa with a wide range of divergence times. However, its parameter space and thus its applicability to a wide taxonomic range has not been systematically studied. Divergence time, population size, time of gene flow, distance of outgroup and number of loci were examined in a sensitivity analysis. The sensitivity study shows that the primary determinant of the D-statistic is the relative population size, i.e. the population size scaled by the number of generations since divergence. This is consistent with the fact that the main confounding factor in gene flow detection is incomplete lineage sorting by diluting the signal. The sensitivity of the D-statistic is also affected by the direction of gene flow, size and number of loci. In addition, we examined the ability of the f-statistics, [Formula: see text] and [Formula: see text], to estimate the fraction of a genome affected by gene flow; while these statistics are difficult to implement to practical questions in biology due to lack of knowledge of when the gene flow happened, they can be used to compare datasets with identical or similar demographic background. The D-statistic, as a method to detect gene flow, is robust against a wide range of genetic distances (divergence times) but it is sensitive to population size. The D-statistic should only be applied with critical reservation to taxa where population sizes are large relative to branch lengths in generations.

Visual and Statistical Analysis of Digital Elevation Models Generated Using Idw Interpolator with Varying Powers

NASA Astrophysics Data System (ADS)

Asal, F. F.

2012-07-01

Digital elevation data obtained from different Engineering Surveying techniques is utilized in generating Digital Elevation Model (DEM), which is employed in many Engineering and Environmental applications. This data is usually in discrete point format making it necessary to utilize an interpolation approach for the creation of DEM. Quality assessment of the DEM is a vital issue controlling its use in different applications; however this assessment relies heavily on statistical methods with neglecting the visual methods. The research applies visual analysis investigation on DEMs generated using IDW interpolator of varying powers in order to examine their potential in the assessment of the effects of the variation of the IDW power on the quality of the DEMs. Real elevation data has been collected from field using total station instrument in a corrugated terrain. DEMs have been generated from the data at a unified cell size using IDW interpolator with power values ranging from one to ten. Visual analysis has been undertaken using 2D and 3D views of the DEM; in addition, statistical analysis has been performed for assessment of the validity of the visual techniques in doing such analysis. Visual analysis has shown that smoothing of the DEM decreases with the increase in the power value till the power of four; however, increasing the power more than four does not leave noticeable changes on 2D and 3D views of the DEM. The statistical analysis has supported these results where the value of the Standard Deviation (SD) of the DEM has increased with increasing the power. More specifically, changing the power from one to two has produced 36% of the total increase (the increase in SD due to changing the power from one to ten) in SD and changing to the powers of three and four has given 60% and 75% respectively. This refers to decrease in DEM smoothing with the increase in the power of the IDW. The study also has shown that applying visual methods supported by statistical analysis has proven good potential in the DEM quality assessment.

A Field-Effect Transistor (FET) model for ASAP

NASA Technical Reports Server (NTRS)

Ming, L.

1965-01-01

The derivation of the circuitry of a field effect transistor (FET) model, the procedure for adapting the model to automated statistical analysis program (ASAP), and the results of applying ASAP on this model are described.

Authorship Attribution.

ERIC Educational Resources Information Center

Holmes, David I.

1994-01-01

Considers problems of quantifying literary style. Examines several variables that may be used as stylistic "fingerprints" of a writer. Reviews work done on statistical analysis of change over time in literary style and applies this technique to the Bible. (CFR)

Population analysis of the cingulum bundle using the tubular surface model for schizophrenia detection

NASA Astrophysics Data System (ADS)

Mohan, Vandana; Sundaramoorthi, Ganesh; Kubicki, Marek; Terry, Douglas; Tannenbaum, Allen

2010-03-01

We propose a novel framework for population analysis of DW-MRI data using the Tubular Surface Model. We focus on the Cingulum Bundle (CB) - a major tract for the Limbic System and the main connection of the Cingulate Gyrus, which has been associated with several aspects of Schizophrenia symptomatology. The Tubular Surface Model represents a tubular surface as a center-line with an associated radius function. It provides a natural way to sample statistics along the length of the fiber bundle and reduces the registration of fiber bundle surfaces to that of 4D curves. We apply our framework to a population of 20 subjects (10 normal, 10 schizophrenic) and obtain excellent results with neural network based classification (90% sensitivity, 95% specificity) as well as unsupervised clustering (k-means). Further, we apply statistical analysis to the feature data and characterize the discrimination ability of local regions of the CB, as a step towards localizing CB regions most relevant to Schizophrenia.

Biomechanical analysis of tension band fixation for olecranon fracture treatment.

PubMed

Kozin, S H; Berglund, L J; Cooney, W P; Morrey, B F; An, K N

1996-01-01

This study assessed the strength of various tension band fixation methods with wire and cable applied to simulated olecranon fractures to compare stability and potential failure or complications between the two. Transverse olecranon fractures were simulated by osteotomy. The fracture was anatomically reduced, and various tension band fixation techniques were applied with monofilament wire or multifilament cable. With a material testing machine load displacement curves were obtained and statistical relevance determined by analysis of variance. Two loading modes were tested: loading on the posterior surface of olecranon to simulate triceps pull and loading on the anterior olecranon tip to recreate a potential compressive loading on the fragment during the resistive flexion. All fixation methods were more resistant to posterior loading than to an anterior load. Individual comparative analysis for various loading conditions concluded that tension band fixation is more resilient to tensile forces exerted by the triceps than compressive forces on the anterior olecranon tip. Neither wire passage anterior to the K-wires nor the multifilament cable provided statistically significant increased stability.

A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data

PubMed Central

Vinaixa, Maria; Samino, Sara; Saez, Isabel; Duran, Jordi; Guinovart, Joan J.; Yanes, Oscar

2012-01-01

Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples. PMID:24957762

A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data.

PubMed

Vinaixa, Maria; Samino, Sara; Saez, Isabel; Duran, Jordi; Guinovart, Joan J; Yanes, Oscar

2012-10-18

Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples.

Noise induced hearing loss of forest workers in Turkey.

PubMed

Tunay, M; Melemez, K

2008-09-01

In this study, a total number of 114 workers who were in 3 different groups in terms of age and work underwent audiometric analysis. In order to determine whether there was a statistically significant difference between the hearing loss levels of the workers who were included in the study, variance analysis was applied with the help of the data obtained as a result of the evaluation. Correlation and regression analysis were applied in order to determine the relations between hearing loss and their age and their time of work. As a result of the variance analysis, statistically significant differences were found at 500, 2000 and 4000 Hz frequencies. The most specific difference was observed among chainsaw machine operators at 4000 Hz frequency, which was determined by the variance analysis. As a result of the correlation analysis, significant relations were found between time of work and hearing loss in 0.01 confidence level and between age and hearing loss in 0.05 confidence level. Forest workers using chainsaw machines should be informed, they should wear or use protective materials and less noising chainsaw machines should be used if possible and workers should undergo audiometric tests when they start work and once a year.

Statistical Analysis of CFD Solutions from the 6th AIAA CFD Drag Prediction Workshop

NASA Technical Reports Server (NTRS)

Derlaga, Joseph M.; Morrison, Joseph H.

2017-01-01

A graphical framework is used for statistical analysis of the results from an extensive N- version test of a collection of Reynolds-averaged Navier-Stokes computational uid dynam- ics codes. The solutions were obtained by code developers and users from North America, Europe, Asia, and South America using both common and custom grid sequencees as well as multiple turbulence models for the June 2016 6th AIAA CFD Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic con guration for this workshop was the Common Research Model subsonic transport wing- body previously used for both the 4th and 5th Drag Prediction Workshops. This work continues the statistical analysis begun in the earlier workshops and compares the results from the grid convergence study of the most recent workshop with previous workshops.

Collagen morphology and texture analysis: from statistics to classification

PubMed Central

Mostaço-Guidolin, Leila B.; Ko, Alex C.-T.; Wang, Fei; Xiang, Bo; Hewko, Mark; Tian, Ganghong; Major, Arkady; Shiomi, Masashi; Sowa, Michael G.

2013-01-01

In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage. PMID:23846580

Biostatistics primer: part I.

PubMed

Overholser, Brian R; Sowinski, Kevin M

2007-12-01

Biostatistics is the application of statistics to biologic data. The field of statistics can be broken down into 2 fundamental parts: descriptive and inferential. Descriptive statistics are commonly used to categorize, display, and summarize data. Inferential statistics can be used to make predictions based on a sample obtained from a population or some large body of information. It is these inferences that are used to test specific research hypotheses. This 2-part review will outline important features of descriptive and inferential statistics as they apply to commonly conducted research studies in the biomedical literature. Part 1 in this issue will discuss fundamental topics of statistics and data analysis. Additionally, some of the most commonly used statistical tests found in the biomedical literature will be reviewed in Part 2 in the February 2008 issue.

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

ERIC Educational Resources Information Center

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A. G.

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are…

Principal Component Analysis: Resources for an Essential Application of Linear Algebra

ERIC Educational Resources Information Center

Pankavich, Stephen; Swanson, Rebecca

2015-01-01

Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields, including statistics,…

Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis

ERIC Educational Resources Information Center

Young, Cristobal; Holsteen, Katherine

2017-01-01

Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all…

Introducing Undergraduate Students to Metabolomics Using a NMR-Based Analysis of Coffee Beans

ERIC Educational Resources Information Center

Sandusky, Peter Olaf

2017-01-01

Metabolomics applies multivariate statistical analysis to sets of high-resolution spectra taken over a population of biologically derived samples. The objective is to distinguish subpopulations within the overall sample population, and possibly also to identify biomarkers. While metabolomics has become part of the standard analytical toolbox in…

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

Statistical Analysis of CFD Solutions from the Fourth AIAA Drag Prediction Workshop

NASA Technical Reports Server (NTRS)

Morrison, Joseph H.

2010-01-01

A graphical framework is used for statistical analysis of the results from an extensive N-version test of a collection of Reynolds-averaged Navier-Stokes computational fluid dynamics codes. The solutions were obtained by code developers and users from the U.S., Europe, Asia, and Russia using a variety of grid systems and turbulence models for the June 2009 4th Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration for this workshop was a new subsonic transport model, the Common Research Model, designed using a modern approach for the wing and included a horizontal tail. The fourth workshop focused on the prediction of both absolute and incremental drag levels for wing-body and wing-body-horizontal tail configurations. This work continues the statistical analysis begun in the earlier workshops and compares the results from the grid convergence study of the most recent workshop with earlier workshops using the statistical framework.

Quantile regression for the statistical analysis of immunological data with many non-detects.

PubMed

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Statistics and Discoveries at the LHC (1/4)

ScienceCinema

Cowan, Glen

2018-02-09

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistics and Discoveries at the LHC (3/4)

ScienceCinema

Cowan, Glen

2018-02-19

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistics and Discoveries at the LHC (4/4)

ScienceCinema

Cowan, Glen

2018-05-22

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistics and Discoveries at the LHC (2/4)

ScienceCinema

Cowan, Glen

2018-04-26

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistical Modeling for Radiation Hardness Assurance

NASA Technical Reports Server (NTRS)

Ladbury, Raymond L.

2014-01-01

We cover the models and statistics associated with single event effects (and total ionizing dose), why we need them, and how to use them: What models are used, what errors exist in real test data, and what the model allows us to say about the DUT will be discussed. In addition, how to use other sources of data such as historical, heritage, and similar part and how to apply experience, physics, and expert opinion to the analysis will be covered. Also included will be concepts of Bayesian statistics, data fitting, and bounding rates.

A weighted U-statistic for genetic association analyses of sequencing data.

PubMed

Wei, Changshuai; Li, Ming; He, Zihuai; Vsevolozhskaya, Olga; Schaid, Daniel J; Lu, Qing

2014-12-01

With advancements in next-generation sequencing technology, a massive amount of sequencing data is generated, which offers a great opportunity to comprehensively investigate the role of rare variants in the genetic etiology of complex diseases. Nevertheless, the high-dimensional sequencing data poses a great challenge for statistical analysis. The association analyses based on traditional statistical methods suffer substantial power loss because of the low frequency of genetic variants and the extremely high dimensionality of the data. We developed a Weighted U Sequencing test, referred to as WU-SEQ, for the high-dimensional association analysis of sequencing data. Based on a nonparametric U-statistic, WU-SEQ makes no assumption of the underlying disease model and phenotype distribution, and can be applied to a variety of phenotypes. Through simulation studies and an empirical study, we showed that WU-SEQ outperformed a commonly used sequence kernel association test (SKAT) method when the underlying assumptions were violated (e.g., the phenotype followed a heavy-tailed distribution). Even when the assumptions were satisfied, WU-SEQ still attained comparable performance to SKAT. Finally, we applied WU-SEQ to sequencing data from the Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very low density lipoprotein cholesterol. © 2014 WILEY PERIODICALS, INC.

GPUs for statistical data analysis in HEP: a performance study of GooFit on GPUs vs. RooFit on CPUs

NASA Astrophysics Data System (ADS)

Pompili, Alexis; Di Florio, Adriano; CMS Collaboration

2016-10-01

In order to test the computing capabilities of GPUs with respect to traditional CPU cores a high-statistics toy Monte Carlo technique has been implemented both in ROOT/RooFit and GooFit frameworks with the purpose to estimate the statistical significance of the structure observed by CMS close to the kinematical boundary of the Jψϕ invariant mass in the three-body decay B +→JψϕK +. GooFit is a data analysis open tool under development that interfaces ROOT/RooFit to CUDA platform on nVidia GPU. The optimized GooFit application running on GPUs hosted by servers in the Bari Tier2 provides striking speed-up performances with respect to the RooFit application parallelised on multiple CPUs by means of PROOF-Lite tool. The considerably resulting speed-up, while comparing concurrent GooFit processes allowed by CUDA Multi Process Service and a RooFit/PROOF-Lite process with multiple CPU workers, is presented and discussed in detail. By means of GooFit it has also been possible to explore the behaviour of a likelihood ratio test statistic in different situations in which the Wilks Theorem may apply or does not apply because its regularity conditions are not satisfied.

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

PubMed Central

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B; Neyer, Franz J; van Aken, Marcel AG

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided. PMID:24116396

Tests of Mediation: Paradoxical Decline in Statistical Power as a Function of Mediator Collinearity

PubMed Central

Beasley, T. Mark

2013-01-01

Increasing the correlation between the independent variable and the mediator (a coefficient) increases the effect size (ab) for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation due to increases in a at some point outweighs the increase of the effect size (ab) and results in a loss of statistical power. This phenomenon also occurs with nonparametric bootstrapping approaches because the variance of the bootstrap distribution of ab approximates the variance expected from normal theory. Both variances increase dramatically when a exceeds the b coefficient, thus explaining the power decline with increases in a. Implications for statistical analysis and applied researchers are discussed. PMID:24954952

[The informational support of statistical observation related to children disability].

PubMed

Son, I M; Polikarpov, A V; Ogrizko, E V; Golubeva, T Yu

2016-01-01

Within the framework of the Convention on rights of the disabled the revision is specified concerning criteria of identification of disability of children and reformation of system of medical social expertise according international standards of indices of health and indices related to health. In connection with it, it is important to consider the relationship between alterations in forms of the Federal statistical monitoring in the part of registration of disabled children in the Russian Federation and classification of health indices and indices related to health applied at identification of disability. The article presents analysis of relationship between alterations in forms of the Federal statistical monitoring in the part of registration of disabled children in the Russian Federation and applied classifications used at identification of disability (International classification of impairments, disabilities and handicap (ICDH), international classification of functioning, disability and health (ICF), international classification of functioning, disability and health, version for children and youth (ICF-CY). The intersectorial interaction is considered within the framework of statistics of children disability.

Confocal Raman microscopy and multivariate statistical analysis for determination of different penetration abilities of caffeine and propylene glycol applied simultaneously in a mixture on porcine skin ex vivo.

PubMed

Mujica Ascencio, Saul; Choe, ChunSik; Meinke, Martina C; Müller, Rainer H; Maksimov, George V; Wigger-Alberti, Walter; Lademann, Juergen; Darvin, Maxim E

2016-07-01

Propylene glycol is one of the known substances added in cosmetic formulations as a penetration enhancer. Recently, nanocrystals have been employed also to increase the skin penetration of active components. Caffeine is a component with many applications and its penetration into the epidermis is controversially discussed in the literature. In the present study, the penetration ability of two components - caffeine nanocrystals and propylene glycol, applied topically on porcine ear skin in the form of a gel, was investigated ex vivo using two confocal Raman microscopes operated at different excitation wavelengths (785nm and 633nm). Several depth profiles were acquired in the fingerprint region and different spectral ranges, i.e., 526-600cm(-1) and 810-880cm(-1) were chosen for independent analysis of caffeine and propylene glycol penetration into the skin, respectively. Multivariate statistical methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) combined with Student's t-test were employed to calculate the maximum penetration depths of each substance (caffeine and propylene glycol). The results show that propylene glycol penetrates significantly deeper than caffeine (20.7-22.0μm versus 12.3-13.0μm) without any penetration enhancement effect on caffeine. The results confirm that different substances, even if applied onto the skin as a mixture, can penetrate differently. The penetration depths of caffeine and propylene glycol obtained using two different confocal Raman microscopes are comparable showing that both types of microscopes are well suited for such investigations and that multivariate statistical PCA-LDA methods combined with Student's t-test are very useful for analyzing the penetration of different substances into the skin. Copyright © 2016 Elsevier B.V. All rights reserved.

RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis.

PubMed

Glaab, Enrico; Schneider, Reinhard

2015-07-01

High-throughput omics datasets often contain technical replicates included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses.We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics. Freely available at http://www.repexplore.tk enrico.glaab@uni.lu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Extracting chemical information from high-resolution Kβ X-ray emission spectroscopy

NASA Astrophysics Data System (ADS)

Limandri, S.; Robledo, J.; Tirao, G.

2018-06-01

High-resolution X-ray emission spectroscopy allows studying the chemical environment of a wide variety of materials. Chemical information can be obtained by fitting the X-ray spectra and observing the behavior of some spectral features. Spectral changes can also be quantified by means of statistical parameters calculated by considering the spectrum as a probability distribution. Another possibility is to perform statistical multivariate analysis, such as principal component analysis. In this work the performance of these procedures for extracting chemical information in X-ray emission spectroscopy spectra for mixtures of Mn2+ and Mn4+ oxides are studied. A detail analysis of the parameters obtained, as well as the associated uncertainties is shown. The methodologies are also applied for Mn oxidation state characterization of double perovskite oxides Ba1+xLa1-xMnSbO6 (with 0 ≤ x ≤ 0.7). The results show that statistical parameters and multivariate analysis are the most suitable for the analysis of this kind of spectra.

Analysis of the interannual variability of tropical cyclones striking the California coast based on statistical downscaling

NASA Astrophysics Data System (ADS)

Mendez, F. J.; Rueda, A.; Barnard, P.; Mori, N.; Nakajo, S.; Espejo, A.; del Jesus, M.; Diez Sierra, J.; Cofino, A. S.; Camus, P.

2016-02-01

Hurricanes hitting California have a very low ocurrence probability due to typically cool ocean temperature and westward tracks. However, damages associated to these improbable events would be dramatic in Southern California and understanding the oceanographic and atmospheric drivers is of paramount importance for coastal risk management for present and future climates. A statistical analysis of the historical events is very difficult due to the limited resolution of atmospheric and oceanographic forcing data available. In this work, we propose a combination of: (a) statistical downscaling methods (Espejo et al, 2015); and (b) a synthetic stochastic tropical cyclone (TC) model (Nakajo et al, 2014). To build the statistical downscaling model, Y=f(X), we apply a combination of principal component analysis and the k-means classification algorithm to find representative patterns from a potential TC index derived from large-scale SST fields in Eastern Central Pacific (predictor X) and the associated tropical cyclone ocurrence (predictand Y). SST data comes from NOAA Extended Reconstructed SST V3b providing information from 1854 to 2013 on a 2.0 degree x 2.0 degree global grid. As data for the historical occurrence and paths of tropical cycloneas are scarce, we apply a stochastic TC model which is based on a Monte Carlo simulation of the joint distribution of track, minimum sea level pressure and translation speed of the historical events in the Eastern Central Pacific Ocean. Results will show the ability of the approach to explain seasonal-to-interannual variability of the predictor X, which is clearly related to El Niño Southern Oscillation. References Espejo, A., Méndez, F.J., Diez, J., Medina, R., Al-Yahyai, S. (2015) Seasonal probabilistic forecasting of tropical cyclone activity in the North Indian Ocean, Journal of Flood Risk Management, DOI: 10.1111/jfr3.12197 Nakajo, S., N. Mori, T. Yasuda, and H. Mase (2014) Global Stochastic Tropical Cyclone Model Based on Principal Component Analysis and Cluster Analysis, Journal of Applied Meteorology and Climatology, DOI: 10.1175/JAMC-D-13-08.1

Solving Differential Equations in R

EPA Science Inventory

Although R is still predominantly applied for statistical analysis and graphical representation, it is rapidly becoming more suitable for mathematical computing. One of the fields where considerable progress has been made recently is the solution of differential equations. Here w...

Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice.

PubMed

Willis, Brian H; Riley, Richard D

2017-09-20

An important question for clinicians appraising a meta-analysis is: are the findings likely to be valid in their own practice-does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity-where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple ('leave-one-out') cross-validation technique, we demonstrate how we may test meta-analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta-analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta-analysis and a tailored meta-regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within-study variance, between-study variance, study sample size, and the number of studies in the meta-analysis. Finally, we apply Vn to two published meta-analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta-analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

A new approach for remediation of As-contaminated soil: ball mill-based technique.

PubMed

Shin, Yeon-Jun; Park, Sang-Min; Yoo, Jong-Chan; Jeon, Chil-Sung; Lee, Seung-Woo; Baek, Kitae

2016-02-01

In this study, a physical ball mill process instead of chemical extraction using toxic chemical agents was applied to remove arsenic (As) from contaminated soil. A statistical analysis was carried out to establish the optimal conditions for ball mill processing. As a result of the statistical analysis, approximately 70% of As was removed from the soil at the following conditions: 5 min, 1.0 cm, 10 rpm, and 5% of operating time, media size, rotational velocity, and soil loading conditions, respectively. A significant amount of As remained in the grinded fine soil after ball mill processing while more than 90% of soil has the original properties to be reused or recycled. As a result, the ball mill process could remove the metals bound strongly to the surface of soil by the surface grinding, which could be applied as a pretreatment before application of chemical extraction to reduce the load.

The Importance of Medical Students' Attitudes Regarding Cognitive Competence for Teaching Applied Statistics: Multi-Site Study and Meta-Analysis

PubMed Central

Milic, Natasa M.; Masic, Srdjan; Milin-Lazovic, Jelena; Trajkovic, Goran; Bukumiric, Zoran; Savic, Marko; Milic, Nikola V.; Cirkovic, Andja; Gajic, Milan; Kostic, Mirjana; Ilic, Aleksandra; Stanisavljevic, Dejana

2016-01-01

Background The scientific community increasingly is recognizing the need to bolster standards of data analysis given the widespread concern that basic mistakes in data analysis are contributing to the irreproducibility of many published research findings. The aim of this study was to investigate students’ attitudes towards statistics within a multi-site medical educational context, monitor their changes and impact on student achievement. In addition, we performed a systematic review to better support our future pedagogical decisions in teaching applied statistics to medical students. Methods A validated Serbian Survey of Attitudes Towards Statistics (SATS-36) questionnaire was administered to medical students attending obligatory introductory courses in biostatistics from three medical universities in the Western Balkans. A systematic review of peer-reviewed publications was performed through searches of Scopus, Web of Science, Science Direct, Medline, and APA databases through 1994. A meta-analysis was performed for the correlation coefficients between SATS component scores and statistics achievement. Pooled estimates were calculated using random effects models. Results SATS-36 was completed by 461 medical students. Most of the students held positive attitudes towards statistics. Ability in mathematics and grade point average were associated in a multivariate regression model with the Cognitive Competence score, after adjusting for age, gender and computer ability. The results of 90 paired data showed that Affect, Cognitive Competence, and Effort scores demonstrated significant positive changes. The Cognitive Competence score showed the largest increase (M = 0.48, SD = 0.95). The positive correlation found between the Cognitive Competence score and students’ achievement (r = 0.41; p<0.001), was also shown in the meta-analysis (r = 0.37; 95% CI 0.32–0.41). Conclusion Students' subjective attitudes regarding Cognitive Competence at the beginning of the biostatistics course, which were directly linked to mathematical knowledge, affected their attitudes at the end of the course that, in turn, influenced students' performance. This indicates the importance of positively changing not only students’ cognitive competency, but also their perceptions of gained competency during the biostatistics course. PMID:27764123

The Importance of Medical Students' Attitudes Regarding Cognitive Competence for Teaching Applied Statistics: Multi-Site Study and Meta-Analysis.

PubMed

Milic, Natasa M; Masic, Srdjan; Milin-Lazovic, Jelena; Trajkovic, Goran; Bukumiric, Zoran; Savic, Marko; Milic, Nikola V; Cirkovic, Andja; Gajic, Milan; Kostic, Mirjana; Ilic, Aleksandra; Stanisavljevic, Dejana

2016-01-01

The scientific community increasingly is recognizing the need to bolster standards of data analysis given the widespread concern that basic mistakes in data analysis are contributing to the irreproducibility of many published research findings. The aim of this study was to investigate students' attitudes towards statistics within a multi-site medical educational context, monitor their changes and impact on student achievement. In addition, we performed a systematic review to better support our future pedagogical decisions in teaching applied statistics to medical students. A validated Serbian Survey of Attitudes Towards Statistics (SATS-36) questionnaire was administered to medical students attending obligatory introductory courses in biostatistics from three medical universities in the Western Balkans. A systematic review of peer-reviewed publications was performed through searches of Scopus, Web of Science, Science Direct, Medline, and APA databases through 1994. A meta-analysis was performed for the correlation coefficients between SATS component scores and statistics achievement. Pooled estimates were calculated using random effects models. SATS-36 was completed by 461 medical students. Most of the students held positive attitudes towards statistics. Ability in mathematics and grade point average were associated in a multivariate regression model with the Cognitive Competence score, after adjusting for age, gender and computer ability. The results of 90 paired data showed that Affect, Cognitive Competence, and Effort scores demonstrated significant positive changes. The Cognitive Competence score showed the largest increase (M = 0.48, SD = 0.95). The positive correlation found between the Cognitive Competence score and students' achievement (r = 0.41; p<0.001), was also shown in the meta-analysis (r = 0.37; 95% CI 0.32-0.41). Students' subjective attitudes regarding Cognitive Competence at the beginning of the biostatistics course, which were directly linked to mathematical knowledge, affected their attitudes at the end of the course that, in turn, influenced students' performance. This indicates the importance of positively changing not only students' cognitive competency, but also their perceptions of gained competency during the biostatistics course.

Evaluation of standardized and applied variables in predicting treatment outcomes of polytrauma patients.

PubMed

Aksamija, Goran; Mulabdic, Adi; Rasic, Ismar; Muhovic, Samir; Gavric, Igor

2011-01-01

Polytrauma is defined as an injury where they are affected by at least two different organ systems or body, with at least one life-threatening injuries. Given the multilevel model care of polytrauma patients within KCUS are inevitable weaknesses in the management of this category of patients. To determine the dynamics of existing procedures in treatment of polytrauma patients on admission to KCUS, and based on statistical analysis of variables applied to determine and define the factors that influence the final outcome of treatment, and determine their mutual relationship, which may result in eliminating the flaws in the approach to the problem. The study was based on 263 polytrauma patients. Parametric and non-parametric statistical methods were used. Basic statistics were calculated, based on the calculated parameters for the final achievement of research objectives, multicoleration analysis, image analysis, discriminant analysis and multifactorial analysis were used. From the universe of variables for this study we selected sample of n = 25 variables, of which the first two modular, others belong to the common measurement space (n = 23) and in this paper defined as a system variable methods, procedures and assessments of polytrauma patients. After the multicoleration analysis, since the image analysis gave a reliable measurement results, we started the analysis of eigenvalues, that is defining the factors upon which they obtain information about the system solve the problem of the existing model and its correlation with treatment outcome. The study singled out the essential factors that determine the current organizational model of care, which may affect the treatment and better outcome of polytrauma patients. This analysis has shown the maximum correlative relationships between these practices and contributed to development guidelines that are defined by isolated factors.

The Design and Development of a Context-Rich, Photo-Based Online Testing to Assess Students' Science Learning

ERIC Educational Resources Information Center

Lin, Min-Jin; Guo, Chorng-Jee; Hsu, Chia-Er

2011-01-01

This study designed and developed a CP-MCT (content-rich, photo-based multiple choice online test) to assess whether college students can apply the basic light concept to interpret daily light phenomena. One hundred college students volunteered to take the CP-MCT, and the results were statistically analyzed by applying t-test or ANOVA (Analysis of…

Introducing 3D U-statistic method for separating anomaly from background in exploration geochemical data with associated software development

NASA Astrophysics Data System (ADS)

Ghannadpour, Seyyed Saeed; Hezarkhani, Ardeshir

2016-03-01

The U-statistic method is one of the most important structural methods to separate the anomaly from the background. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and determine anomalous areas. In the present study, to use U-statistic method in three-dimensional (3D) condition, U-statistic is applied on the grade of two ideal test examples, by considering sample Z values (elevation). So far, this is the first time that this method has been applied on a 3D condition. To evaluate the performance of 3D U-statistic method and in order to compare U-statistic with one non-structural method, the method of threshold assessment based on median and standard deviation (MSD method) is applied on the two example tests. Results show that the samples indicated by U-statistic method as anomalous are more regular and involve less dispersion than those indicated by the MSD method. So that, according to the location of anomalous samples, denser areas of them can be determined as promising zones. Moreover, results show that at a threshold of U = 0, the total error of misclassification for U-statistic method is much smaller than the total error of criteria of bar {x}+n× s. Finally, 3D model of two test examples for separating anomaly from background using 3D U-statistic method is provided. The source code for a software program, which was developed in the MATLAB programming language in order to perform the calculations of the 3D U-spatial statistic method, is additionally provided. This software is compatible with all the geochemical varieties and can be used in similar exploration projects.

The data life cycle applied to our own data.

PubMed

Goben, Abigail; Raszewski, Rebecca

2015-01-01

Increased demand for data-driven decision making is driving the need for librarians to be facile with the data life cycle. This case study follows the migration of reference desk statistics from handwritten to digital format. This shift presented two opportunities: first, the availability of a nonsensitive data set to improve the librarians' understanding of data-management and statistical analysis skills, and second, the use of analytics to directly inform staffing decisions and departmental strategic goals. By working through each step of the data life cycle, library faculty explored data gathering, storage, sharing, and analysis questions.

Application of Allan Deviation to Assessing Uncertainties of Continuous-measurement Instruments, and Optimizing Calibration Schemes

NASA Astrophysics Data System (ADS)

Jacobson, Gloria; Rella, Chris; Farinas, Alejandro

2014-05-01

Technological advancement of instrumentation in atmospheric and other geoscience disciplines over the past decade has lead to a shift from discrete sample analysis to continuous, in-situ monitoring. Standard error analysis used for discrete measurements is not sufficient to assess and compare the error contribution of noise and drift from continuous-measurement instruments, and a different statistical analysis approach should be applied. The Allan standard deviation analysis technique developed for atomic clock stability assessment by David W. Allan [1] can be effectively and gainfully applied to continuous measurement instruments. As an example, P. Werle et al has applied these techniques to look at signal averaging for atmospheric monitoring by Tunable Diode-Laser Absorption Spectroscopy (TDLAS) [2]. This presentation will build on, and translate prior foundational publications to provide contextual definitions and guidelines for the practical application of this analysis technique to continuous scientific measurements. The specific example of a Picarro G2401 Cavity Ringdown Spectroscopy (CRDS) analyzer used for continuous, atmospheric monitoring of CO2, CH4 and CO will be used to define the basics features the Allan deviation, assess factors affecting the analysis, and explore the time-series to Allan deviation plot translation for different types of instrument noise (white noise, linear drift, and interpolated data). In addition, the useful application of using an Allan deviation to optimize and predict the performance of different calibration schemes will be presented. Even though this presentation will use the specific example of the Picarro G2401 CRDS Analyzer for atmospheric monitoring, the objective is to present the information such that it can be successfully applied to other instrument sets and disciplines. [1] D.W. Allan, "Statistics of Atomic Frequency Standards," Proc, IEEE, vol. 54, pp 221-230, Feb 1966 [2] P. Werle, R. Miicke, F. Slemr, "The Limits of Signal Averaging in Atmospheric Trace-Gas Monitoring by Tunable Diode-Laser Absorption Spectroscopy (TDLAS)," Applied Physics, B57, pp 131-139, April 1993

Tipping point analysis of atmospheric oxygen concentration

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

Livina, V. N.; Forbes, A. B.; Vaz Martins, T. M.

2015-03-15

We apply tipping point analysis to nine observational oxygen concentration records around the globe, analyse their dynamics and perform projections under possible future scenarios, leading to oxygen deficiency in the atmosphere. The analysis is based on statistical physics framework with stochastic modelling, where we represent the observed data as a composition of deterministic and stochastic components estimated from the observed data using Bayesian and wavelet techniques.

The Importance of Variance in Statistical Analysis: Don't Throw Out the Baby with the Bathwater.

ERIC Educational Resources Information Center

Peet, Martha W.

This paper analyzes what happens to the effect size of a given dataset when the variance is removed by categorization for the purpose of applying "OVA" methods (analysis of variance, analysis of covariance). The dataset is from a classic study by Holzinger and Swinefors (1939) in which more than 20 ability test were administered to 301…

Surface inspection of flat products by means of texture analysis: on-line implementation using neural networks

NASA Astrophysics Data System (ADS)

Fernandez, Carlos; Platero, Carlos; Campoy, Pascual; Aracil, Rafael

1994-11-01

This paper describes some texture-based techniques that can be applied to quality assessment of flat products continuously produced (metal strips, wooden surfaces, cork, textile products, ...). Since the most difficult task is that of inspecting for product appearance, human-like inspection ability is required. A common feature to all these products is the presence of non- deterministic texture on their surfaces. Two main subjects are discussed: statistical techniques for both surface finishing determination and surface defect analysis as well as real-time implementation for on-line inspection in high-speed applications. For surface finishing determination a Gray Level Difference technique is presented to perform over low resolution images, that is, no-zoomed images. Defect analysis is performed by means of statistical texture analysis over defective portions of the surface. On-line implementation is accomplished by means of neural networks. When a defect arises, textural analysis is applied which result in a data-vector, acting as input of a neural net, previously trained in a supervised way. This approach tries to reach on-line performance in automated visual inspection applications when texture is presented in flat product surfaces.

Content analysis to detect high stress in oral interviews and text documents

NASA Technical Reports Server (NTRS)

Thirumalainambi, Rajkumar (Inventor); Jorgensen, Charles C. (Inventor)

2012-01-01

A system of interrogation to estimate whether a subject of interrogation is likely experiencing high stress, emotional volatility and/or internal conflict in the subject's responses to an interviewer's questions. The system applies one or more of four procedures, a first statistical analysis, a second statistical analysis, a third analysis and a heat map analysis, to identify one or more documents containing the subject's responses for which further examination is recommended. Words in the documents are characterized in terms of dimensions representing different classes of emotions and states of mind, in which the subject's responses that manifest high stress, emotional volatility and/or internal conflict are identified. A heat map visually displays the dimensions manifested by the subject's responses in different colors, textures, geometric shapes or other visually distinguishable indicia.

Source apportionment of groundwater pollution around landfill site in Nagpur, India.

PubMed

Pujari, Paras R; Deshpande, Vijaya

2005-12-01

The present work attempts statistical analysis of groundwater quality near a Landfill site in Nagpur, India. The objective of the present work is to figure out the impact of different factors on the quality of groundwater in the study area. Statistical analysis of the data has been attempted by applying Factor Analysis concept. The analysis brings out the effect of five different factors governing the groundwater quality in the study area. Based on the contribution of the different parameters present in the extracted factors, the latter are linked to the geological setting, the leaching from the host rock, leachate of heavy metals from the landfill as well as the bacterial contamination from landfill site and other anthropogenic activities. The analysis brings out the vulnerability of the unconfined aquifer to contamination.

Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

NASA Astrophysics Data System (ADS)

Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

2017-04-01

Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums shows a mean improvement of more than 40% in CRPS when compared to bilinearly interpolated uncalibrated ensemble forecasts. The validation on randomly selected grid points, representing the true height distribution over Austria, still indicates a mean improvement of 35%. The applied statistical model is currently set up for 6-hourly and daily accumulation periods, but will be extended to a temporal resolution of 1-3 hours within a new probabilistic nowcasting system operated by ZAMG.

UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework

NASA Astrophysics Data System (ADS)

Rubin, D.; Aldering, G.; Barbary, K.; Boone, K.; Chappell, G.; Currie, M.; Deustua, S.; Fagrelius, P.; Fruchter, A.; Hayden, B.; Lidman, C.; Nordin, J.; Perlmutter, S.; Saunders, C.; Sofiatti, C.; Supernova Cosmology Project, The

2015-11-01

While recent supernova (SN) cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current SN cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, unexplained dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real SN observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was primarily performed blinded, in that the basic framework was first validated on simulated data before transitioning to real data. We also discuss possible extensions of the method.

Reliability analysis of composite structures

NASA Technical Reports Server (NTRS)

Kan, Han-Pin

1992-01-01

A probabilistic static stress analysis methodology has been developed to estimate the reliability of a composite structure. Closed form stress analysis methods are the primary analytical tools used in this methodology. These structural mechanics methods are used to identify independent variables whose variations significantly affect the performance of the structure. Once these variables are identified, scatter in their values is evaluated and statistically characterized. The scatter in applied loads and the structural parameters are then fitted to appropriate probabilistic distribution functions. Numerical integration techniques are applied to compute the structural reliability. The predicted reliability accounts for scatter due to variability in material strength, applied load, fabrication and assembly processes. The influence of structural geometry and mode of failure are also considerations in the evaluation. Example problems are given to illustrate various levels of analytical complexity.

STRengthening analytical thinking for observational studies: the STRATOS initiative.

PubMed

Sauerbrei, Willi; Abrahamowicz, Michal; Altman, Douglas G; le Cessie, Saskia; Carpenter, James

2014-12-30

The validity and practical utility of observational medical research depends critically on good study design, excellent data quality, appropriate statistical methods and accurate interpretation of results. Statistical methodology has seen substantial development in recent times. Unfortunately, many of these methodological developments are ignored in practice. Consequently, design and analysis of observational studies often exhibit serious weaknesses. The lack of guidance on vital practical issues discourages many applied researchers from using more sophisticated and possibly more appropriate methods when analyzing observational studies. Furthermore, many analyses are conducted by researchers with a relatively weak statistical background and limited experience in using statistical methodology and software. Consequently, even 'standard' analyses reported in the medical literature are often flawed, casting doubt on their results and conclusions. An efficient way to help researchers to keep up with recent methodological developments is to develop guidance documents that are spread to the research community at large. These observations led to the initiation of the strengthening analytical thinking for observational studies (STRATOS) initiative, a large collaboration of experts in many different areas of biostatistical research. The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies. The guidance is intended for applied statisticians and other data analysts with varying levels of statistical education, experience and interests. In this article, we introduce the STRATOS initiative and its main aims, present the need for guidance documents and outline the planned approach and progress so far. We encourage other biostatisticians to become involved. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

Uncertainty Analysis of Seebeck Coefficient and Electrical Resistivity Characterization

NASA Technical Reports Server (NTRS)

Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred

2014-01-01

In order to provide a complete description of a materials thermoelectric power factor, in addition to the measured nominal value, an uncertainty interval is required. The uncertainty may contain sources of measurement error including systematic bias error and precision error of a statistical nature. The work focuses specifically on the popular ZEM-3 (Ulvac Technologies) measurement system, but the methods apply to any measurement system. The analysis accounts for sources of systematic error including sample preparation tolerance, measurement probe placement, thermocouple cold-finger effect, and measurement parameters; in addition to including uncertainty of a statistical nature. Complete uncertainty analysis of a measurement system allows for more reliable comparison of measurement data between laboratories.

Application of modified profile analysis to function testing of the motion/no-motion issue in an aircraft ground-handling simulation. [statistical analysis procedure for man machine systems flight simulation

NASA Technical Reports Server (NTRS)

Parrish, R. V.; Mckissick, B. T.; Steinmetz, G. G.

1979-01-01

A recent modification of the methodology of profile analysis, which allows the testing for differences between two functions as a whole with a single test, rather than point by point with multiple tests is discussed. The modification is applied to the examination of the issue of motion/no motion conditions as shown by the lateral deviation curve as a function of engine cut speed of a piloted 737-100 simulator. The results of this application are presented along with those of more conventional statistical test procedures on the same simulator data.

Detecting subtle hydrochemical anomalies with multivariate statistics: an example from homogeneous groundwaters in the Great Artesian Basin, Australia

NASA Astrophysics Data System (ADS)

O'Shea, Bethany; Jankowski, Jerzy

2006-12-01

The major ion composition of Great Artesian Basin groundwater in the lower Namoi River valley is relatively homogeneous in chemical composition. Traditional graphical techniques have been combined with multivariate statistical methods to determine whether subtle differences in the chemical composition of these waters can be delineated. Hierarchical cluster analysis and principal components analysis were successful in delineating minor variations within the groundwaters of the study area that were not visually identified in the graphical techniques applied. Hydrochemical interpretation allowed geochemical processes to be identified in each statistically defined water type and illustrated how these groundwaters differ from one another. Three main geochemical processes were identified in the groundwaters: ion exchange, precipitation, and mixing between waters from different sources. Both statistical methods delineated an anomalous sample suspected of being influenced by magmatic CO2 input. The use of statistical methods to complement traditional graphical techniques for waters appearing homogeneous is emphasized for all investigations of this type. Copyright

[Relativity among starch quantity, polysaccharides content and total alkaloid content of Dendrobium loddigesii].

PubMed

Zhu, Hua; Teng, Jianbei; Cai, Yi; Liang, Jie; Zhu, Yilin; Wei, Tao

2011-12-01

To find out the relativity among starch quantity, polysaccharides content and total alkaloid content of Dendrobium loddigesii. Microscopy-counting process was applied to starch quantity statistics, sulfuric acid-anthrone colorimetry was used to assay polysaccharides content and bromocresol green colorimetry was used to assay alkaloid content. Pearson product moment correlation analysis, Kendall's rank correlation analysis and Spearman's concordance coefficient analysis were applied to study their relativity. Extremely significant positive correlation was found between starch quantity and polysaccharides content, and significant negative correlation between alkaloid content and starch quantity was discovered, as well was between alkaloid content and polysaccharides content.

Regression modeling of ground-water flow

USGS Publications Warehouse

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Different modes of data processing and statistical testing applied to the same set of pharmaco-EEG recordings: effects on the evaluation of a selective and reversible MAO A inhibitor (brofaromine).

PubMed

Reimann, I W; Jobert, M; Gleiter, C H; Turri, M; Bieck, P R; Herrmann, W M

1996-01-01

The comparison of two different modes of data processing and two different approaches to statistical testing both applied to the same set of EEG recordings was the main objective of this pharmacological study. Brofaromine (CGP 11,305 A), a new selective and reversible monoamine oxidase type A inhibitor was used as an example for investigating a potentially antidepressant drug in clinical development. The two modes of pharmaco-EEG (PEEG) data processing differed mainly in the sampling frequency and definition of spectral parameters. Patterns of significant changes were noted in terms of descriptive data analysis using either a nonparametric Wilcoxon signed-rank test or an ANOVA of transformed data, as suggested by Conover and Iman. These data clearly demonstrate that slight discrepancies in the results may simply arise from differences in data processing and statistical approach applied. In spite of these discrepancies, the pattern of brofaromine-induced PEEG changes was very similar regardless of the mode of data handling used.

Protein Sectors: Statistical Coupling Analysis versus Conservation

PubMed Central

Teşileanu, Tiberiu; Colwell, Lucy J.; Leibler, Stanislas

2015-01-01

Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed “sectors”. The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation. PMID:25723535

Statistical Characteristics of Single Sort of Grape Bulgarian Wines

NASA Astrophysics Data System (ADS)

Boyadzhiev, D.

2008-10-01

The aim of this paper is to evaluate the differences in the values of the 8 basic physicochemical indices of single sort of grape Bulgarian wines (white and red ones), obligatory for the standardization of ready production in the winery. Statistically significant differences in the values of various sorts and vintages are established and possibilities for identifying the sort and the vintage on the base of these indices by applying discriminant analysis are discussed.

Statistics for Time-Series Spatial Data: Applying Survival Analysis to Study Land-Use Change

ERIC Educational Resources Information Center

Wang, Ninghua Nathan

2013-01-01

Traditional spatial analysis and data mining methods fall short of extracting temporal information from data. This inability makes their use difficult to study changes and the associated mechanisms of many geographic phenomena of interest, for example, land-use. On the other hand, the growing availability of land-change data over multiple time…

Explore the Usefulness of Person-Fit Analysis on Large-Scale Assessment

ERIC Educational Resources Information Center

Cui, Ying; Mousavi, Amin

2015-01-01

The current study applied the person-fit statistic, l[subscript z], to data from a Canadian provincial achievement test to explore the usefulness of conducting person-fit analysis on large-scale assessments. Item parameter estimates were compared before and after the misfitting student responses, as identified by l[subscript z], were removed. The…

Analysis of Time-Series Quasi-Experiments. Final Report.

ERIC Educational Resources Information Center

Glass, Gene V.; Maguire, Thomas O.

The objective of this project was to investigate the adequacy of statistical models developed by G. E. P. Box and G. C. Tiao for the analysis of time-series quasi-experiments: (1) The basic model developed by Box and Tiao is applied to actual time-series experiment data from two separate experiments, one in psychology and one in educational…

Digital Natives, Digital Immigrants: An Analysis of Age and ICT Competency in Teacher Education

ERIC Educational Resources Information Center

Guo, Ruth Xiaoqing; Dobson, Teresa; Petrina, Stephen

2008-01-01

This article examines the intersection of age and ICT (information and communication technology) competency and critiques the "digital natives versus digital immigrants" argument proposed by Prensky (2001a, 2001b). Quantitative analysis was applied to a statistical data set collected in the context of a study with over 2,000 pre-service…

Investigating Faculty Familiarity with Assessment Terminology by Applying Cluster Analysis to Interpret Survey Data

ERIC Educational Resources Information Center

Raker, Jeffrey R.; Holme, Thomas A.

2014-01-01

A cluster analysis was conducted with a set of survey data on chemistry faculty familiarity with 13 assessment terms. Cluster groupings suggest a high, middle, and low overall familiarity with the terminology and an independent high and low familiarity with terms related to fundamental statistics. The six resultant clusters were found to be…

A Quantitative Features Analysis of Recommended No- and Low-Cost Preschool E-Books

ERIC Educational Resources Information Center

Parette, Howard P.; Blum, Craig; Luthin, Katie

2015-01-01

In recent years, recommended e-books have drawn increasing attention from early childhood education professionals. This study applied a quantitative descriptive features analysis of cost (n = 70) and no-cost (n = 60) e-books recommended by the Texas Computer Education Association. While t tests revealed no statistically significant differences…

STRengthening Analytical Thinking for Observational Studies: the STRATOS initiative

PubMed Central

Sauerbrei, Willi; Abrahamowicz, Michal; Altman, Douglas G; le Cessie, Saskia; Carpenter, James

2014-01-01

The validity and practical utility of observational medical research depends critically on good study design, excellent data quality, appropriate statistical methods and accurate interpretation of results. Statistical methodology has seen substantial development in recent times. Unfortunately, many of these methodological developments are ignored in practice. Consequently, design and analysis of observational studies often exhibit serious weaknesses. The lack of guidance on vital practical issues discourages many applied researchers from using more sophisticated and possibly more appropriate methods when analyzing observational studies. Furthermore, many analyses are conducted by researchers with a relatively weak statistical background and limited experience in using statistical methodology and software. Consequently, even ‘standard’ analyses reported in the medical literature are often flawed, casting doubt on their results and conclusions. An efficient way to help researchers to keep up with recent methodological developments is to develop guidance documents that are spread to the research community at large. These observations led to the initiation of the strengthening analytical thinking for observational studies (STRATOS) initiative, a large collaboration of experts in many different areas of biostatistical research. The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies. The guidance is intended for applied statisticians and other data analysts with varying levels of statistical education, experience and interests. In this article, we introduce the STRATOS initiative and its main aims, present the need for guidance documents and outline the planned approach and progress so far. We encourage other biostatisticians to become involved. PMID:25074480

Senior Computational Scientist | Center for Cancer Research

Cancer.gov

The Basic Science Program (BSP) pursues independent, multidisciplinary research in basic and applied molecular biology, immunology, retrovirology, cancer biology, and human genetics. Research efforts and support are an integral part of the Center for Cancer Research (CCR) at the Frederick National Laboratory for Cancer Research (FNLCR). The Cancer & Inflammation Program (CIP), Basic Science Program, HLA Immunogenetics Section, under the leadership of Dr. Mary Carrington, studies the influence of human leukocyte antigens (HLA) and specific KIR/HLA genotypes on risk of and outcomes to infection, cancer, autoimmune disease, and maternal-fetal disease. Recent studies have focused on the impact of HLA gene expression in disease, the molecular mechanism regulating expression levels, and the functional basis for the effect of differential expression on disease outcome. The lab’s further focus is on the genetic basis for resistance/susceptibility to disease conferred by immunogenetic variation. KEY ROLES/RESPONSIBILITIES The Senior Computational Scientist will provide research support to the CIP-BSP-HLA Immunogenetics Section performing bio-statistical design, analysis and reporting of research projects conducted in the lab. This individual will be involved in the implementation of statistical models and data preparation. Successful candidate should have 5 or more years of competent, innovative biostatistics/bioinformatics research experience, beyond doctoral training Considerable experience with statistical software, such as SAS, R and S-Plus Sound knowledge, and demonstrated experience of theoretical and applied statistics Write program code to analyze data using statistical analysis software Contribute to the interpretation and publication of research results

Statistical analysis to assess automated level of suspicion scoring methods in breast ultrasound

NASA Astrophysics Data System (ADS)

Galperin, Michael

2003-05-01

A well-defined rule-based system has been developed for scoring 0-5 the Level of Suspicion (LOS) based on qualitative lexicon describing the ultrasound appearance of breast lesion. The purposes of the research are to asses and select one of the automated LOS scoring quantitative methods developed during preliminary studies in benign biopsies reduction. The study has used Computer Aided Imaging System (CAIS) to improve the uniformity and accuracy of applying the LOS scheme by automatically detecting, analyzing and comparing breast masses. The overall goal is to reduce biopsies on the masses with lower levels of suspicion, rather that increasing the accuracy of diagnosis of cancers (will require biopsy anyway). On complex cysts and fibroadenoma cases experienced radiologists were up to 50% less certain in true negatives than CAIS. Full correlation analysis was applied to determine which of the proposed LOS quantification methods serves CAIS accuracy the best. This paper presents current results of applying statistical analysis for automated LOS scoring quantification for breast masses with known biopsy results. It was found that First Order Ranking method yielded most the accurate results. The CAIS system (Image Companion, Data Companion software) is developed by Almen Laboratories and was used to achieve the results.

Statistical process control methods allow the analysis and improvement of anesthesia care.

PubMed

Fasting, Sigurd; Gisvold, Sven E

2003-10-01

Quality aspects of the anesthetic process are reflected in the rate of intraoperative adverse events. The purpose of this report is to illustrate how the quality of the anesthesia process can be analyzed using statistical process control methods, and exemplify how this analysis can be used for quality improvement. We prospectively recorded anesthesia-related data from all anesthetics for five years. The data included intraoperative adverse events, which were graded into four levels, according to severity. We selected four adverse events, representing important quality and safety aspects, for statistical process control analysis. These were: inadequate regional anesthesia, difficult emergence from general anesthesia, intubation difficulties and drug errors. We analyzed the underlying process using 'p-charts' for statistical process control. In 65,170 anesthetics we recorded adverse events in 18.3%; mostly of lesser severity. Control charts were used to define statistically the predictable normal variation in problem rate, and then used as a basis for analysis of the selected problems with the following results: Inadequate plexus anesthesia: stable process, but unacceptably high failure rate; Difficult emergence: unstable process, because of quality improvement efforts; Intubation difficulties: stable process, rate acceptable; Medication errors: methodology not suited because of low rate of errors. By applying statistical process control methods to the analysis of adverse events, we have exemplified how this allows us to determine if a process is stable, whether an intervention is required, and if quality improvement efforts have the desired effect.

Statistical Analysis of Human Body Movement and Group Interactions in Response to Music

NASA Astrophysics Data System (ADS)

Desmet, Frank; Leman, Marc; Lesaffre, Micheline; de Bruyn, Leen

Quantification of time series that relate to physiological data is challenging for empirical music research. Up to now, most studies have focused on time-dependent responses of individual subjects in controlled environments. However, little is known about time-dependent responses of between-subject interactions in an ecological context. This paper provides new findings on the statistical analysis of group synchronicity in response to musical stimuli. Different statistical techniques were applied to time-dependent data obtained from an experiment on embodied listening in individual and group settings. Analysis of inter group synchronicity are described. Dynamic Time Warping (DTW) and Cross Correlation Function (CCF) were found to be valid methods to estimate group coherence of the resulting movements. It was found that synchronicity of movements between individuals (human-human interactions) increases significantly in the social context. Moreover, Analysis of Variance (ANOVA) revealed that the type of music is the predominant factor in both the individual and the social context.

Valid Statistical Analysis for Logistic Regression with Multiple Sources

NASA Astrophysics Data System (ADS)

Fienberg, Stephen E.; Nardi, Yuval; Slavković, Aleksandra B.

Considerable effort has gone into understanding issues of privacy protection of individual information in single databases, and various solutions have been proposed depending on the nature of the data, the ways in which the database will be used and the precise nature of the privacy protection being offered. Once data are merged across sources, however, the nature of the problem becomes far more complex and a number of privacy issues arise for the linked individual files that go well beyond those that are considered with regard to the data within individual sources. In the paper, we propose an approach that gives full statistical analysis on the combined database without actually combining it. We focus mainly on logistic regression, but the method and tools described may be applied essentially to other statistical models as well.

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.

Statistical analysis and interpolation of compositional data in materials science.

PubMed

Pesenson, Misha Z; Suram, Santosh K; Gregoire, John M

2015-02-09

Compositional data are ubiquitous in chemistry and materials science: analysis of elements in multicomponent systems, combinatorial problems, etc., lead to data that are non-negative and sum to a constant (for example, atomic concentrations). The constant sum constraint restricts the sampling space to a simplex instead of the usual Euclidean space. Since statistical measures such as mean and standard deviation are defined for the Euclidean space, traditional correlation studies, multivariate analysis, and hypothesis testing may lead to erroneous dependencies and incorrect inferences when applied to compositional data. Furthermore, composition measurements that are used for data analytics may not include all of the elements contained in the material; that is, the measurements may be subcompositions of a higher-dimensional parent composition. Physically meaningful statistical analysis must yield results that are invariant under the number of composition elements, requiring the application of specialized statistical tools. We present specifics and subtleties of compositional data processing through discussion of illustrative examples. We introduce basic concepts, terminology, and methods required for the analysis of compositional data and utilize them for the spatial interpolation of composition in a sputtered thin film. The results demonstrate the importance of this mathematical framework for compositional data analysis (CDA) in the fields of materials science and chemistry.

Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice

PubMed Central

Riley, Richard D.

2017-01-01

An important question for clinicians appraising a meta‐analysis is: are the findings likely to be valid in their own practice—does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity—where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (‘leave‐one‐out’) cross‐validation technique, we demonstrate how we may test meta‐analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta‐analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta‐analysis and a tailored meta‐regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within‐study variance, between‐study variance, study sample size, and the number of studies in the meta‐analysis. Finally, we apply Vn to two published meta‐analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta‐analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28620945

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

DOE PAGES

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

2018-01-01

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

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

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-ling; Xiong, Momiao; Fan, Ruzong

2017-01-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data. PMID:28000696

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.

PubMed

Chiu, Chi-Yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-Ling; Xiong, Momiao; Fan, Ruzong

2017-02-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

A spatial scan statistic for survival data based on Weibull distribution.

PubMed

Bhatt, Vijaya; Tiwari, Neeraj

2014-05-20

The spatial scan statistic has been developed as a geographical cluster detection analysis tool for different types of data sets such as Bernoulli, Poisson, ordinal, normal and exponential. We propose a scan statistic for survival data based on Weibull distribution. It may also be used for other survival distributions, such as exponential, gamma, and log normal. The proposed method is applied on the survival data of tuberculosis patients for the years 2004-2005 in Nainital district of Uttarakhand, India. Simulation studies reveal that the proposed method performs well for different survival distribution functions. Copyright © 2013 John Wiley & Sons, Ltd.

Ozone data and mission sampling analysis

NASA Technical Reports Server (NTRS)

Robbins, J. L.

1980-01-01

A methodology was developed to analyze discrete data obtained from the global distribution of ozone. Statistical analysis techniques were applied to describe the distribution of data variance in terms of empirical orthogonal functions and components of spherical harmonic models. The effects of uneven data distribution and missing data were considered. Data fill based on the autocorrelation structure of the data is described. Computer coding of the analysis techniques is included.

Congruence analysis of geodetic networks - hypothesis tests versus model selection by information criteria

NASA Astrophysics Data System (ADS)

Lehmann, Rüdiger; Lösler, Michael

2017-12-01

Geodetic deformation analysis can be interpreted as a model selection problem. The null model indicates that no deformation has occurred. It is opposed to a number of alternative models, which stipulate different deformation patterns. A common way to select the right model is the usage of a statistical hypothesis test. However, since we have to test a series of deformation patterns, this must be a multiple test. As an alternative solution for the test problem, we propose the p-value approach. Another approach arises from information theory. Here, the Akaike information criterion (AIC) or some alternative is used to select an appropriate model for a given set of observations. Both approaches are discussed and applied to two test scenarios: A synthetic levelling network and the Delft test data set. It is demonstrated that they work but behave differently, sometimes even producing different results. Hypothesis tests are well-established in geodesy, but may suffer from an unfavourable choice of the decision error rates. The multiple test also suffers from statistical dependencies between the test statistics, which are neglected. Both problems are overcome by applying information criterions like AIC.

A statistical analysis of the impact of advertising signs on road safety.

PubMed

Yannis, George; Papadimitriou, Eleonora; Papantoniou, Panagiotis; Voulgari, Chrisoula

2013-01-01

This research aims to investigate the impact of advertising signs on road safety. An exhaustive review of international literature was carried out on the effect of advertising signs on driver behaviour and safety. Moreover, a before-and-after statistical analysis with control groups was applied on several road sites with different characteristics in the Athens metropolitan area, in Greece, in order to investigate the correlation between the placement or removal of advertising signs and the related occurrence of road accidents. Road accident data for the 'before' and 'after' periods on the test sites and the control sites were extracted from the database of the Hellenic Statistical Authority, and the selected 'before' and 'after' periods vary from 2.5 to 6 years. The statistical analysis shows no statistical correlation between road accidents and advertising signs in none of the nine sites examined, as the confidence intervals of the estimated safety effects are non-significant at 95% confidence level. This can be explained by the fact that, in the examined road sites, drivers are overloaded with information (traffic signs, directions signs, labels of shops, pedestrians and other vehicles, etc.) so that the additional information load from advertising signs may not further distract them.

Testing statistical isotropy in cosmic microwave background polarization maps

NASA Astrophysics Data System (ADS)

Rath, Pranati K.; Samal, Pramoda Kumar; Panda, Srikanta; Mishra, Debesh D.; Aluri, Pavan K.

2018-04-01

We apply our symmetry based Power tensor technique to test conformity of PLANCK Polarization maps with statistical isotropy. On a wide range of angular scales (l = 40 - 150), our preliminary analysis detects many statistically anisotropic multipoles in foreground cleaned full sky PLANCK polarization maps viz., COMMANDER and NILC. We also study the effect of residual foregrounds that may still be present in the Galactic plane using both common UPB77 polarization mask, as well as the individual component separation method specific polarization masks. However, some of the statistically anisotropic modes still persist, albeit significantly in NILC map. We further probed the data for any coherent alignments across multipoles in several bins from the chosen multipole range.

A Tutorial in Bayesian Potential Outcomes Mediation Analysis.

PubMed

Miočević, Milica; Gonzalez, Oscar; Valente, Matthew J; MacKinnon, David P

2018-01-01

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

"The Two Brothers": Reconciling Perceptual-Cognitive and Statistical Models of Musical Evolution.

PubMed

Jan, Steven

2018-01-01

While the "units, events and dynamics" of memetic evolution have been abstractly theorized (Lynch, 1998), they have not been applied systematically to real corpora in music. Some researchers, convinced of the validity of cultural evolution in more than the metaphorical sense adopted by much musicology, but perhaps skeptical of some or all of the claims of memetics, have attempted statistically based corpus-analysis techniques of music drawn from molecular biology, and these have offered strong evidence in favor of system-level change over time (Savage, 2017). This article argues that such statistical approaches, while illuminating, ignore the psychological realities of music-information grouping, the transmission of such groups with varying degrees of fidelity, their selection according to relative perceptual-cognitive salience, and the power of this Darwinian process to drive the systemic changes (such as the development over time of systems of tonal organization in music) that statistical methodologies measure. It asserts that a synthesis between such statistical approaches to the study of music-cultural change and the theory of memetics as applied to music (Jan, 2007), in particular the latter's perceptual-cognitive elements, would harness the strengths of each approach and deepen understanding of cultural evolution in music.

“The Two Brothers”: Reconciling Perceptual-Cognitive and Statistical Models of Musical Evolution

PubMed Central

Jan, Steven

2018-01-01

While the “units, events and dynamics” of memetic evolution have been abstractly theorized (Lynch, 1998), they have not been applied systematically to real corpora in music. Some researchers, convinced of the validity of cultural evolution in more than the metaphorical sense adopted by much musicology, but perhaps skeptical of some or all of the claims of memetics, have attempted statistically based corpus-analysis techniques of music drawn from molecular biology, and these have offered strong evidence in favor of system-level change over time (Savage, 2017). This article argues that such statistical approaches, while illuminating, ignore the psychological realities of music-information grouping, the transmission of such groups with varying degrees of fidelity, their selection according to relative perceptual-cognitive salience, and the power of this Darwinian process to drive the systemic changes (such as the development over time of systems of tonal organization in music) that statistical methodologies measure. It asserts that a synthesis between such statistical approaches to the study of music-cultural change and the theory of memetics as applied to music (Jan, 2007), in particular the latter's perceptual-cognitive elements, would harness the strengths of each approach and deepen understanding of cultural evolution in music. PMID:29670551

Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis

NASA Astrophysics Data System (ADS)

Vouterakos, P. A.; Moustris, K. P.; Bartzokas, A.; Ziomas, I. C.; Nastos, P. T.; Paliatsos, A. G.

2012-12-01

In this work, artificial neural networks (ANNs) were developed and applied in order to forecast the discomfort levels due to the combination of high temperature and air humidity, during the hot season of the year, in eight different regions within the Greater Athens area (GAA), Greece. For the selection of the best type and architecture of ANNs-forecasting models, the multiple criteria analysis (MCA) technique was applied. Three different types of ANNs were developed and tested with the MCA method. Concretely, the multilayer perceptron, the generalized feed forward networks (GFFN), and the time-lag recurrent networks were developed and tested. Results showed that the best ANNs type performance was achieved by using the GFFN model for the prediction of discomfort levels due to high temperature and air humidity within GAA. For the evaluation of the constructed ANNs, appropriate statistical indices were used. The analysis proved that the forecasting ability of the developed ANNs models is very satisfactory at a significant statistical level of p < 0.01.

qFeature

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

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Statistical analysis of sperm sorting

NASA Astrophysics Data System (ADS)

Koh, James; Marcos, Marcos

2017-11-01

The success rate of assisted reproduction depends on the proportion of morphologically normal sperm. It is possible to use an external field for manipulation and sorting. Depending on their morphology, the extent of response varies. Due to the wide distribution in sperm morphology even among individuals, the resulting distribution of kinematic behaviour, and consequently the feasibility of sorting, should be analysed statistically. In this theoretical work, Resistive Force Theory and Slender Body Theory will be applied and compared. Full name is Marcos.

MRMC analysis of agreement studies

NASA Astrophysics Data System (ADS)

Gallas, Brandon D.; Anam, Amrita; Chen, Weijie; Wunderlich, Adam; Zhang, Zhiwei

2016-03-01

The purpose of this work is to present and evaluate methods based on U-statistics to compare intra- or inter-reader agreement across different imaging modalities. We apply these methods to multi-reader multi-case (MRMC) studies. We measure reader-averaged agreement and estimate its variance accounting for the variability from readers and cases (an MRMC analysis). In our application, pathologists (readers) evaluate patient tissue mounted on glass slides (cases) in two ways. They evaluate the slides on a microscope (reference modality) and they evaluate digital scans of the slides on a computer display (new modality). In the current work, we consider concordance as the agreement measure, but many of the concepts outlined here apply to other agreement measures. Concordance is the probability that two readers rank two cases in the same order. Concordance can be estimated with a U-statistic and thus it has some nice properties: it is unbiased, asymptotically normal, and its variance is given by an explicit formula. Another property of a U-statistic is that it is symmetric in its inputs; it doesn't matter which reader is listed first or which case is listed first, the result is the same. Using this property and a few tricks while building the U-statistic kernel for concordance, we get a mathematically tractable problem and efficient software. Simulations show that our variance and covariance estimates are unbiased.

Effect of citric acid, tetracycline, and doxycycline on instrumented periodontally involved root surfaces: A SEM study

PubMed Central

Chahal, Gurparkash Singh; Chhina, Kamalpreet; Chhabra, Vipin; Bhatnagar, Rakhi; Chahal, Amna

2014-01-01

Background: A surface smear layer consisting of organic and inorganic material is formed on the root surface following mechanical instrumentation and may inhibit the formation of new connective tissue attachment to the root surface. Modification of the tooth surface by root conditioning has resulted in improved connective tissue attachment and has advanced the goal of reconstructive periodontal treatment. Aim: The aim of this study was to compare the effects of citric acid, tetracycline, and doxycycline on the instrumented periodontally involved root surfaces in vitro using a scanning electron microscope. Settings and Design: A total of 45 dentin samples obtained from 15 extracted, scaled, and root planed teeth were divided into three groups. Materials and Methods: The root conditioning agents were applied with cotton pellets using the Passive burnishing technique for 5 minutes. The samples were then examined by the scanning electron microscope. Statistical Analysis Used: The statistical analysis was carried out using Statistical Package for Social Sciences (SPSS Inc., Chicago, IL, version 15.0 for Windows). For all quantitative variables means and standard deviations were calculated and compared. For more than two groups ANOVA was applied. For multiple comparisons post hoc tests with Bonferroni correction was used. Results: Upon statistical analysis the root conditioning agents used in this study were found to be effective in removing the smear layer, uncovering and widening the dentin tubules and unmasking the dentin collagen matrix. Conclusion: Tetracycline HCl was found to be the best root conditioner among the three agents used. PMID:24744541

Students' attitudes towards learning statistics

NASA Astrophysics Data System (ADS)

Ghulami, Hassan Rahnaward; Hamid, Mohd Rashid Ab; Zakaria, Roslinazairimah

2015-05-01

Positive attitude towards learning is vital in order to master the core content of the subject matters under study. This is unexceptional in learning statistics course especially at the university level. Therefore, this study investigates the students' attitude towards learning statistics. Six variables or constructs have been identified such as affect, cognitive competence, value, difficulty, interest, and effort. The instrument used for the study is questionnaire that was adopted and adapted from the reliable instrument of Survey of Attitudes towards Statistics(SATS©). This study is conducted to engineering undergraduate students in one of the university in the East Coast of Malaysia. The respondents consist of students who were taking the applied statistics course from different faculties. The results are analysed in terms of descriptive analysis and it contributes to the descriptive understanding of students' attitude towards the teaching and learning process of statistics.

Students' Successes and Challenges Applying Data Analysis and Measurement Skills in a Fifth-Grade Integrated STEM Unit

ERIC Educational Resources Information Center

Glancy, Aran W.; Moore, Tamara J.; Guzey, Selcen; Smith, Karl A.

2017-01-01

An understanding of statistics and skills in data analysis are becoming more and more essential, yet research consistently shows that students struggle with these concepts at all levels. This case study documents some of the struggles four groups of fifth-grade students encounter as they collect, organize, and interpret data and then ultimately…

Statistical analysis of arsenic contamination in drinking water in a city of Iran and its modeling using GIS.

PubMed

Sadeghi, Fatemeh; Nasseri, Simin; Mosaferi, Mohammad; Nabizadeh, Ramin; Yunesian, Masud; Mesdaghinia, Alireza

2017-05-01

In this research, probable arsenic contamination in drinking water in the city of Ardabil was studied in 163 samples during four seasons. In each season, sampling was carried out randomly in the study area. Results were analyzed statistically applying SPSS 19 software, and the data was also modeled by Arc GIS 10.1 software. The maximum permissible arsenic concentration in drinking water defined by the World Health Organization and Iranian national standard is 10 μg/L. Statistical analysis showed 75, 88, 47, and 69% of samples in autumn, winter, spring, and summer, respectively, had concentrations higher than the national standard. The mean concentrations of arsenic in autumn, winter, spring, and summer were 19.89, 15.9, 10.87, and 14.6 μg/L, respectively, and the overall average in all samples through the year was 15.32 μg/L. Although GIS outputs indicated that the concentration distribution profiles changed in four consecutive seasons, variance analysis of the results showed that statistically there is no significant difference in arsenic levels in four seasons.

A standards-based method for compositional analysis by energy dispersive X-ray spectrometry using multivariate statistical analysis: application to multicomponent alloys.

PubMed

Rathi, Monika; Ahrenkiel, S P; Carapella, J J; Wanlass, M W

2013-02-01

Given an unknown multicomponent alloy, and a set of standard compounds or alloys of known composition, can one improve upon popular standards-based methods for energy dispersive X-ray (EDX) spectrometry to quantify the elemental composition of the unknown specimen? A method is presented here for determining elemental composition of alloys using transmission electron microscopy-based EDX with appropriate standards. The method begins with a discrete set of related reference standards of known composition, applies multivariate statistical analysis to those spectra, and evaluates the compositions with a linear matrix algebra method to relate the spectra to elemental composition. By using associated standards, only limited assumptions about the physical origins of the EDX spectra are needed. Spectral absorption corrections can be performed by providing an estimate of the foil thickness of one or more reference standards. The technique was applied to III-V multicomponent alloy thin films: composition and foil thickness were determined for various III-V alloys. The results were then validated by comparing with X-ray diffraction and photoluminescence analysis, demonstrating accuracy of approximately 1% in atomic fraction.

Blood pressure and heart rate response to posteriorly directed pressure applied to the cervical spine in young, pain-free individuals: a randomized, repeated-measures, double-blind, placebo-controlled study.

PubMed

Yung, Emmanuel; Wong, Michael; Williams, Haddie; Mache, Kyle

2014-08-01

Randomized clinical trial. Objectives To compare the blood pressure (BP) and heart rate (HR) response of healthy volunteers to posteriorly directed (anterior-to-posterior [AP]) pressure applied to the cervical spine versus placebo. Manual therapists employ cervical spine AP mobilizations for various cervical-shoulder pain conditions. However, there is a paucity of literature describing the procedure, cardiovascular response, and safety profile. Thirty-nine (25 female) healthy participants (mean ± SD age, 24.7 ± 1.9 years) were randomly assigned to 1 of 2 groups. Group 1 received a placebo, consisting of light touch applied to the right C6 costal process. Group 2 received AP pressure at the same location. Blood pressure and HR were measured prior to, during, and after the application of AP pressure. One-way analysis of variance and paired-difference statistics were used for data analysis. There was no statistically significant difference between groups for mean systolic BP, mean diastolic BP, and mean HR (P >.05) for all time points. Within-group comparisons indicated statistically significant differences between baseline and post-AP pressure HR (-2.8 bpm; 95% confidence interval: -4.6, -1.1) and between baseline and post-AP pressure systolic BP (-2.4 mmHg; 95% confidence interval: -3.7, -1.0) in the AP group, and between baseline and postplacebo systolic BP (-2.6 mmHg; 95% confidence interval: -4.2, -1.0) in the placebo group. No participants reported any adverse reactions or side effects within 24 hours of testing. AP pressure caused a statistically significant physiologic response that resulted in a minor drop in HR (without causing asystole or vasodepression) after the procedure, whereas this cardiovascular change did not occur for those in the placebo group. Within both groups, there was a small but statistically significant reduction in systolic BP following the procedure.

Metrology in health: a pilot study

NASA Astrophysics Data System (ADS)

Ferreira, M.; Matos, A.

2015-02-01

The purpose of this paper is to identify and analyze some relevant issues which arise when the concept of metrological traceability is applied to health care facilities. Discussion is structured around the results that were obtained through a characterization and comparative description of the practices applied in 45 different Portuguese health entities. Following a qualitative exploratory approach, the information collected was the support for the initial research hypotheses and the development of the questionnaire survey. It was also applied a quantitative methodology that included a descriptive and inferential statistical analysis of the experimental data set.

On the structure and phase transitions of power-law Poissonian ensembles

NASA Astrophysics Data System (ADS)

Eliazar, Iddo; Oshanin, Gleb

2012-10-01

Power-law Poissonian ensembles are Poisson processes that are defined on the positive half-line, and that are governed by power-law intensities. Power-law Poissonian ensembles are stochastic objects of fundamental significance; they uniquely display an array of fractal features and they uniquely generate a span of important applications. In this paper we apply three different methods—oligarchic analysis, Lorenzian analysis and heterogeneity analysis—to explore power-law Poissonian ensembles. The amalgamation of these analyses, combined with the topology of power-law Poissonian ensembles, establishes a detailed and multi-faceted picture of the statistical structure and the statistical phase transitions of these elemental ensembles.

Sunspot activity and influenza pandemics: a statistical assessment of the purported association.

PubMed

Towers, S

2017-10-01

Since 1978, a series of papers in the literature have claimed to find a significant association between sunspot activity and the timing of influenza pandemics. This paper examines these analyses, and attempts to recreate the three most recent statistical analyses by Ertel (1994), Tapping et al. (2001), and Yeung (2006), which all have purported to find a significant relationship between sunspot numbers and pandemic influenza. As will be discussed, each analysis had errors in the data. In addition, in each analysis arbitrary selections or assumptions were also made, and the authors did not assess the robustness of their analyses to changes in those arbitrary assumptions. Varying the arbitrary assumptions to other, equally valid, assumptions negates the claims of significance. Indeed, an arbitrary selection made in one of the analyses appears to have resulted in almost maximal apparent significance; changing it only slightly yields a null result. This analysis applies statistically rigorous methodology to examine the purported sunspot/pandemic link, using more statistically powerful un-binned analysis methods, rather than relying on arbitrarily binned data. The analyses are repeated using both the Wolf and Group sunspot numbers. In all cases, no statistically significant evidence of any association was found. However, while the focus in this particular analysis was on the purported relationship of influenza pandemics to sunspot activity, the faults found in the past analyses are common pitfalls; inattention to analysis reproducibility and robustness assessment are common problems in the sciences, that are unfortunately not noted often enough in review.

The skeletal maturation status estimated by statistical shape analysis: axial images of Japanese cervical vertebra.

PubMed

Shin, S M; Kim, Y-I; Choi, Y-S; Yamaguchi, T; Maki, K; Cho, B-H; Park, S-B

2015-01-01

To evaluate axial cervical vertebral (ACV) shape quantitatively and to build a prediction model for skeletal maturation level using statistical shape analysis for Japanese individuals. The sample included 24 female and 19 male patients with hand-wrist radiographs and CBCT images. Through generalized Procrustes analysis and principal components (PCs) analysis, the meaningful PCs were extracted from each ACV shape and analysed for the estimation regression model. Each ACV shape had meaningful PCs, except for the second axial cervical vertebra. Based on these models, the smallest prediction intervals (PIs) were from the combination of the shape space PCs, age and gender. Overall, the PIs of the male group were smaller than those of the female group. There was no significant correlation between centroid size as a size factor and skeletal maturation level. Our findings suggest that the ACV maturation method, which was applied by statistical shape analysis, could confirm information about skeletal maturation in Japanese individuals as an available quantifier of skeletal maturation and could be as useful a quantitative method as the skeletal maturation index.

The skeletal maturation status estimated by statistical shape analysis: axial images of Japanese cervical vertebra

PubMed Central

Shin, S M; Choi, Y-S; Yamaguchi, T; Maki, K; Cho, B-H; Park, S-B

2015-01-01

Objectives: To evaluate axial cervical vertebral (ACV) shape quantitatively and to build a prediction model for skeletal maturation level using statistical shape analysis for Japanese individuals. Methods: The sample included 24 female and 19 male patients with hand–wrist radiographs and CBCT images. Through generalized Procrustes analysis and principal components (PCs) analysis, the meaningful PCs were extracted from each ACV shape and analysed for the estimation regression model. Results: Each ACV shape had meaningful PCs, except for the second axial cervical vertebra. Based on these models, the smallest prediction intervals (PIs) were from the combination of the shape space PCs, age and gender. Overall, the PIs of the male group were smaller than those of the female group. There was no significant correlation between centroid size as a size factor and skeletal maturation level. Conclusions: Our findings suggest that the ACV maturation method, which was applied by statistical shape analysis, could confirm information about skeletal maturation in Japanese individuals as an available quantifier of skeletal maturation and could be as useful a quantitative method as the skeletal maturation index. PMID:25411713

Analysis of regional deformation and strain accumulation data adjacent to the San Andreas fault

NASA Technical Reports Server (NTRS)

Turcotte, Donald L.

1991-01-01

A new approach to the understanding of crustal deformation was developed under this grant. This approach combined aspects of fractals, chaos, and self-organized criticality to provide a comprehensive theory for deformation on distributed faults. It is hypothesized that crustal deformation is an example of comminution: Deformation takes place on a fractal distribution of faults resulting in a fractal distribution of seismicity. Our primary effort under this grant was devoted to developing an understanding of distributed deformation in the continental crust. An initial effort was carried out on the fractal clustering of earthquakes in time. It was shown that earthquakes do not obey random Poisson statistics, but can be approximated in many cases by coupled, scale-invariant fractal statistics. We applied our approach to the statistics of earthquakes in the New Hebrides region of the southwest Pacific because of the very high level of seismicity there. This work was written up and published in the Bulletin of the Seismological Society of America. This approach was also applied to the statistics of the seismicity on the San Andreas fault system.

How to Perform a Systematic Review and Meta-analysis of Diagnostic Imaging Studies.

PubMed

Cronin, Paul; Kelly, Aine Marie; Altaee, Duaa; Foerster, Bradley; Petrou, Myria; Dwamena, Ben A

2018-05-01

A systematic review is a comprehensive search, critical evaluation, and synthesis of all the relevant studies on a specific (clinical) topic that can be applied to the evaluation of diagnostic and screening imaging studies. It can be a qualitative or a quantitative (meta-analysis) review of available literature. A meta-analysis uses statistical methods to combine and summarize the results of several studies. In this review, a 12-step approach to performing a systematic review (and meta-analysis) is outlined under the four domains: (1) Problem Formulation and Data Acquisition, (2) Quality Appraisal of Eligible Studies, (3) Statistical Analysis of Quantitative Data, and (4) Clinical Interpretation of the Evidence. This review is specifically geared toward the performance of a systematic review and meta-analysis of diagnostic test accuracy (imaging) studies. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis.

PubMed

Reese, Sarah E; Archer, Kellie J; Therneau, Terry M; Atkinson, Elizabeth J; Vachon, Celine M; de Andrade, Mariza; Kocher, Jean-Pierre A; Eckel-Passow, Jeanette E

2013-11-15

Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal component analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data. We present an extension of PCA to quantify the existence of batch effects, called guided PCA (gPCA). We describe a test statistic that uses gPCA to test whether a batch effect exists. We apply our proposed test statistic derived using gPCA to simulated data and to two copy number variation case studies: the first study consisted of 614 samples from a breast cancer family study using Illumina Human 660 bead-chip arrays, whereas the second case study consisted of 703 samples from a family blood pressure study that used Affymetrix SNP Array 6.0. We demonstrate that our statistic has good statistical properties and is able to identify significant batch effects in two copy number variation case studies. We developed a new statistic that uses gPCA to identify whether batch effects exist in high-throughput genomic data. Although our examples pertain to copy number data, gPCA is general and can be used on other data types as well. The gPCA R package (Available via CRAN) provides functionality and data to perform the methods in this article. reesese@vcu.edu

Categorization of the trophic status of a hydroelectric power plant reservoir in the Brazilian Amazon by statistical analyses and fuzzy approaches.

PubMed

da Costa Lobato, Tarcísio; Hauser-Davis, Rachel Ann; de Oliveira, Terezinha Ferreira; Maciel, Marinalva Cardoso; Tavares, Maria Regina Madruga; da Silveira, Antônio Morais; Saraiva, Augusto Cesar Fonseca

2015-02-15

The Amazon area has been increasingly suffering from anthropogenic impacts, especially due to the construction of hydroelectric power plant reservoirs. The analysis and categorization of the trophic status of these reservoirs are of interest to indicate man-made changes in the environment. In this context, the present study aimed to categorize the trophic status of a hydroelectric power plant reservoir located in the Brazilian Amazon by constructing a novel Water Quality Index (WQI) and Trophic State Index (TSI) for the reservoir using major ion concentrations and physico-chemical water parameters determined in the area and taking into account the sampling locations and the local hydrological regimes. After applying statistical analyses (factor analysis and cluster analysis) and establishing a rule base of a fuzzy system to these indicators, the results obtained by the proposed method were then compared to the generally applied Carlson and a modified Lamparelli trophic state index (TSI), specific for trophic regions. The categorization of the trophic status by the proposed fuzzy method was shown to be more reliable, since it takes into account the specificities of the study area, while the Carlson and Lamparelli TSI do not, and, thus, tend to over or underestimate the trophic status of these ecosystems. The statistical techniques proposed and applied in the present study, are, therefore, relevant in cases of environmental management and policy decision-making processes, aiding in the identification of the ecological status of water bodies. With this, it is possible to identify which factors should be further investigated and/or adjusted in order to attempt the recovery of degraded water bodies. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Mixed-Methods Research in Nutrition and Dietetics.

PubMed

Zoellner, Jamie; Harris, Jeffrey E

2017-05-01

This work focuses on mixed-methods research (MMR) and is the 11th in a series exploring the importance of research design, statistical analysis, and epidemiologic methods as applied to nutrition and dietetics research. MMR research is an investigative technique that applies both quantitative and qualitative data. The purpose of this article is to define MMR; describe its history and nature; provide reasons for its use; describe and explain the six different MMR designs; describe sample selection; and provide guidance in data collection, analysis, and inference. MMR concepts are applied and integrated with nutrition-related scenarios in real-world research contexts and summary recommendations are provided. Copyright © 2017 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

A novel bi-level meta-analysis approach: applied to biological pathway analysis.

PubMed

Nguyen, Tin; Tagett, Rebecca; Donato, Michele; Mitrea, Cristina; Draghici, Sorin

2016-02-01

The accumulation of high-throughput data in public repositories creates a pressing need for integrative analysis of multiple datasets from independent experiments. However, study heterogeneity, study bias, outliers and the lack of power of available methods present real challenge in integrating genomic data. One practical drawback of many P-value-based meta-analysis methods, including Fisher's, Stouffer's, minP and maxP, is that they are sensitive to outliers. Another drawback is that, because they perform just one statistical test for each individual experiment, they may not fully exploit the potentially large number of samples within each study. We propose a novel bi-level meta-analysis approach that employs the additive method and the Central Limit Theorem within each individual experiment and also across multiple experiments. We prove that the bi-level framework is robust against bias, less sensitive to outliers than other methods, and more sensitive to small changes in signal. For comparative analysis, we demonstrate that the intra-experiment analysis has more power than the equivalent statistical test performed on a single large experiment. For pathway analysis, we compare the proposed framework versus classical meta-analysis approaches (Fisher's, Stouffer's and the additive method) as well as against a dedicated pathway meta-analysis package (MetaPath), using 1252 samples from 21 datasets related to three human diseases, acute myeloid leukemia (9 datasets), type II diabetes (5 datasets) and Alzheimer's disease (7 datasets). Our framework outperforms its competitors to correctly identify pathways relevant to the phenotypes. The framework is sufficiently general to be applied to any type of statistical meta-analysis. The R scripts are available on demand from the authors. sorin@wayne.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Shot Group Statistics for Small Arms Applications

DTIC Science & Technology

2017-06-01

standard deviation. Analysis is presented as applied to one , n-round shot group and then is extended to treat multiple, n-round shot groups. A...dispersion measure for multiple, n-round shot groups can be constructed by selecting one of the dispersion measures listed above, measuring the dispersion of...as applied to one , n-round shot group and then is extended to treat multiple, n-round shot groups. A dispersion measure for multiple, n- round shot

Multivariate Statistical Approach Applied to Sediment Source Tracking Through Quantification and Mineral Identification, Cheyenne River, South Dakota

NASA Astrophysics Data System (ADS)

Valder, J.; Kenner, S.; Long, A.

2008-12-01

Portions of the Cheyenne River are characterized as impaired by the U.S. Environmental Protection Agency because of water-quality exceedences. The Cheyenne River watershed includes the Black Hills National Forest and part of the Badlands National Park. Preliminary analysis indicates that the Badlands National Park is a major contributor to the exceedances of the water-quality constituents for total dissolved solids and total suspended solids. Water-quality data have been collected continuously since 2007, and in the second year of collection (2008), monthly grab and passive sediment samplers are being used to collect total suspended sediment and total dissolved solids in both base-flow and runoff-event conditions. In addition, sediment samples from the river channel, including bed, bank, and floodplain, have been collected. These samples are being analyzed at the South Dakota School of Mines and Technology's X-Ray Diffraction Lab to quantify the mineralogy of the sediments. A multivariate statistical approach (including principal components, least squares, and maximum likelihood techniques) is applied to the mineral percentages that were characterized for each site to identify the contributing source areas that are causing exceedances of sediment transport in the Cheyenne River watershed. Results of the multivariate analysis demonstrate the likely sources of solids found in the Cheyenne River samples. A further refinement of the methods is in progress that utilizes a conceptual model which, when applied with the multivariate statistical approach, provides a better estimate for sediment sources.

Multivariate analysis of heavy metal contamination using river sediment cores of Nankan River, northern Taiwan

NASA Astrophysics Data System (ADS)

Lee, An-Sheng; Lu, Wei-Li; Huang, Jyh-Jaan; Chang, Queenie; Wei, Kuo-Yen; Lin, Chin-Jung; Liou, Sofia Ya Hsuan

2016-04-01

Through the geology and climate characteristic in Taiwan, generally rivers carry a lot of suspended particles. After these particles settled, they become sediments which are good sorbent for heavy metals in river system. Consequently, sediments can be found recording contamination footprint at low flow energy region, such as estuary. Seven sediment cores were collected along Nankan River, northern Taiwan, which is seriously contaminated by factory, household and agriculture input. Physico-chemical properties of these cores were derived from Itrax-XRF Core Scanner and grain size analysis. In order to interpret these complex data matrices, the multivariate statistical techniques (cluster analysis, factor analysis and discriminant analysis) were introduced to this study. Through the statistical determination, the result indicates four types of sediment. One of them represents contamination event which shows high concentration of Cu, Zn, Pb, Ni and Fe, and low concentration of Si and Zr. Furthermore, three possible contamination sources of this type of sediment were revealed by Factor Analysis. The combination of sediment analysis and multivariate statistical techniques used provides new insights into the contamination depositional history of Nankan River and could be similarly applied to other river systems to determine the scale of anthropogenic contamination.

1H NMR-based metabolic profiling for evaluating poppy seed rancidity and brewing.

PubMed

Jawień, Ewa; Ząbek, Adam; Deja, Stanisław; Łukaszewicz, Marcin; Młynarz, Piotr

2015-12-01

Poppy seeds are widely used in household and commercial confectionery. The aim of this study was to demonstrate the application of metabolic profiling for industrial monitoring of the molecular changes which occur during minced poppy seed rancidity and brewing processes performed on raw seeds. Both forms of poppy seeds were obtained from a confectionery company. Proton nuclear magnetic resonance (1H NMR) was applied as the analytical method of choice together with multivariate statistical data analysis. Metabolic fingerprinting was applied as a bioprocess control tool to monitor rancidity with the trajectory of change and brewing progressions. Low molecular weight compounds were found to be statistically significant biomarkers of these bioprocesses. Changes in concentrations of chemical compounds were explained relative to the biochemical processes and external conditions. The obtained results provide valuable and comprehensive information to gain a better understanding of the biology of rancidity and brewing processes, while demonstrating the potential for applying NMR spectroscopy combined with multivariate data analysis tools for quality control in food industries involved in the processing of oilseeds. This precious and versatile information gives a better understanding of the biology of these processes.

Single photon detection and signal analysis for high sensitivity dosimetry based on optically stimulated luminescence with beryllium oxide

NASA Astrophysics Data System (ADS)

Radtke, J.; Sponner, J.; Jakobi, C.; Schneider, J.; Sommer, M.; Teichmann, T.; Ullrich, W.; Henniger, J.; Kormoll, T.

2018-01-01

Single photon detection applied to optically stimulated luminescence (OSL) dosimetry is a promising approach due to the low level of luminescence light and the known statistical behavior of single photon events. Time resolved detection allows to apply a variety of different and independent data analysis methods. Furthermore, using amplitude modulated stimulation impresses time- and frequency information into the OSL light and therefore allows for additional means of analysis. Considering the impressed frequency information, data analysis by using Fourier transform algorithms or other digital filters can be used for separating the OSL signal from unwanted light or events generated by other phenomena. This potentially lowers the detection limits of low dose measurements and might improve the reproducibility and stability of obtained data. In this work, an OSL system based on a single photon detector, a fast and accurate stimulation unit and an FPGA is presented. Different analysis algorithms which are applied to the single photon data are discussed.

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

Application of econometric and ecology analysis methods in physics software

NASA Astrophysics Data System (ADS)

Han, Min Cheol; Hoff, Gabriela; Kim, Chan Hyeong; Kim, Sung Hun; Grazia Pia, Maria; Ronchieri, Elisabetta; Saracco, Paolo

2017-10-01

Some data analysis methods typically used in econometric studies and in ecology have been evaluated and applied in physics software environments. They concern the evolution of observables through objective identification of change points and trends, and measurements of inequality, diversity and evenness across a data set. Within each analysis area, various statistical tests and measures have been examined. This conference paper summarizes a brief overview of some of these methods.

Meta-analysis of thirty-two case-control and two ecological radon studies of lung cancer.

PubMed

Dobrzynski, Ludwik; Fornalski, Krzysztof W; Reszczynska, Joanna

2018-03-01

A re-analysis has been carried out of thirty-two case-control and two ecological studies concerning the influence of radon, a radioactive gas, on the risk of lung cancer. Three mathematically simplest dose-response relationships (models) were tested: constant (zero health effect), linear, and parabolic (linear-quadratic). Health effect end-points reported in the analysed studies are odds ratios or relative risk ratios, related either to morbidity or mortality. In our preliminary analysis, we show that the results of dose-response fitting are qualitatively (within uncertainties, given as error bars) the same, whichever of these health effect end-points are applied. Therefore, we deemed it reasonable to aggregate all response data into the so-called Relative Health Factor and jointly analysed such mixed data, to obtain better statistical power. In the second part of our analysis, robust Bayesian and classical methods of analysis were applied to this combined dataset. In this part of our analysis, we selected different subranges of radon concentrations. In view of substantial differences between the methodology used by the authors of case-control and ecological studies, the mathematical relationships (models) were applied mainly to the thirty-two case-control studies. The degree to which the two ecological studies, analysed separately, affect the overall results when combined with the thirty-two case-control studies, has also been evaluated. In all, as a result of our meta-analysis of the combined cohort, we conclude that the analysed data concerning radon concentrations below ~1000 Bq/m3 (~20 mSv/year of effective dose to the whole body) do not support the thesis that radon may be a cause of any statistically significant increase in lung cancer incidence.

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Gulgundi, Mohammad Shahid; Shetty, Amba

2018-03-01

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

Statistical Analysis of the Uncertainty in Pre-Flight Aerodynamic Database of a Hypersonic Vehicle

NASA Astrophysics Data System (ADS)

Huh, Lynn

The objective of the present research was to develop a new method to derive the aerodynamic coefficients and the associated uncertainties for flight vehicles via post- flight inertial navigation analysis using data from the inertial measurement unit. Statistical estimates of vehicle state and aerodynamic coefficients are derived using Monte Carlo simulation. Trajectory reconstruction using the inertial navigation system (INS) is a simple and well used method. However, deriving realistic uncertainties in the reconstructed state and any associated parameters is not so straight forward. Extended Kalman filters, batch minimum variance estimation and other approaches have been used. However, these methods generally depend on assumed physical models, assumed statistical distributions (usually Gaussian) or have convergence issues for non-linear problems. The approach here assumes no physical models, is applicable to any statistical distribution, and does not have any convergence issues. The new approach obtains the statistics directly from a sufficient number of Monte Carlo samples using only the generally well known gyro and accelerometer specifications and could be applied to the systems of non-linear form and non-Gaussian distribution. When redundant data are available, the set of Monte Carlo simulations are constrained to satisfy the redundant data within the uncertainties specified for the additional data. The proposed method was applied to validate the uncertainty in the pre-flight aerodynamic database of the X-43A Hyper-X research vehicle. In addition to gyro and acceleration data, the actual flight data include redundant measurements of position and velocity from the global positioning system (GPS). The criteria derived from the blend of the GPS and INS accuracy was used to select valid trajectories for statistical analysis. The aerodynamic coefficients were derived from the selected trajectories by either direct extraction method based on the equations in dynamics, or by the inquiry of the pre-flight aerodynamic database. After the application of the proposed method to the case of the X-43A Hyper-X research vehicle, it was found that 1) there were consistent differences in the aerodynamic coefficients from the pre-flight aerodynamic database and post-flight analysis, 2) the pre-flight estimation of the pitching moment coefficients was significantly different from the post-flight analysis, 3) the type of distribution of the states from the Monte Carlo simulation were affected by that of the perturbation parameters, 4) the uncertainties in the pre-flight model were overestimated, 5) the range where the aerodynamic coefficients from the pre-flight aerodynamic database and post-flight analysis are in closest agreement is between Mach *.* and *.* and more data points may be needed between Mach * and ** in the pre-flight aerodynamic database, 6) selection criterion for valid trajectories from the Monte Carlo simulations was mostly driven by the horizontal velocity error, 7) the selection criterion must be based on reasonable model to ensure the validity of the statistics from the proposed method, and 8) the results from the proposed method applied to the two different flights with the identical geometry and similar flight profile were consistent.

Analysis of Information Content in High-Spectral Resolution Sounders using Subset Selection Analysis

NASA Technical Reports Server (NTRS)

Velez-Reyes, Miguel; Joiner, Joanna

1998-01-01

In this paper, we summarize the results of the sensitivity analysis and data reduction carried out to determine the information content of AIRS and IASI channels. The analysis and data reduction was based on the use of subset selection techniques developed in the linear algebra and statistical community to study linear dependencies in high dimensional data sets. We applied the subset selection method to study dependency among channels by studying the dependency among their weighting functions. Also, we applied the technique to study the information provided by the different levels in which the atmosphere is discretized for retrievals and analysis. Results from the method correlate well with intuition in many respects and point out to possible modifications for band selection in sensor design and number and location of levels in the analysis process.

[Application of chemometrics in composition-activity relationship research of traditional Chinese medicine].

PubMed

Han, Sheng-Nan

2014-07-01

Chemometrics is a new branch of chemistry which is widely applied to various fields of analytical chemistry. Chemometrics can use theories and methods of mathematics, statistics, computer science and other related disciplines to optimize the chemical measurement process and maximize access to acquire chemical information and other information on material systems by analyzing chemical measurement data. In recent years, traditional Chinese medicine has attracted widespread attention. In the research of traditional Chinese medicine, it has been a key problem that how to interpret the relationship between various chemical components and its efficacy, which seriously restricts the modernization of Chinese medicine. As chemometrics brings the multivariate analysis methods into the chemical research, it has been applied as an effective research tool in the composition-activity relationship research of Chinese medicine. This article reviews the applications of chemometrics methods in the composition-activity relationship research in recent years. The applications of multivariate statistical analysis methods (such as regression analysis, correlation analysis, principal component analysis, etc. ) and artificial neural network (such as back propagation artificial neural network, radical basis function neural network, support vector machine, etc. ) are summarized, including the brief fundamental principles, the research contents and the advantages and disadvantages. Finally, the existing main problems and prospects of its future researches are proposed.

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

PubMed Central

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P.

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis. PMID:23586021

Numerical 3D flow simulation of attached cavitation structures at ultrasonic horn tips and statistical evaluation of flow aggressiveness via load collectives

NASA Astrophysics Data System (ADS)

Mottyll, S.; Skoda, R.

2015-12-01

A compressible inviscid flow solver with barotropic cavitation model is applied to two different ultrasonic horn set-ups and compared to hydrophone, shadowgraphy as well as erosion test data. The statistical analysis of single collapse events in wall-adjacent flow regions allows the determination of the flow aggressiveness via load collectives (cumulative event rate vs collapse pressure), which show an exponential decrease in agreement to studies on hydrodynamic cavitation [1]. A post-processing projection of event rate and collapse pressure on a reference grid reduces the grid dependency significantly. In order to evaluate the erosion-sensitive areas a statistical analysis of transient wall loads is utilised. Predicted erosion sensitive areas as well as temporal pressure and vapour volume evolution are in good agreement to the experimental data.

Detection of reflecting surfaces by a statistical model

NASA Astrophysics Data System (ADS)

He, Qiang; Chu, Chee-Hung H.

2009-02-01

Remote sensing is widely used assess the destruction from natural disasters and to plan relief and recovery operations. How to automatically extract useful features and segment interesting objects from digital images, including remote sensing imagery, becomes a critical task for image understanding. Unfortunately, current research on automated feature extraction is ignorant of contextual information. As a result, the fidelity of populating attributes corresponding to interesting features and objects cannot be satisfied. In this paper, we present an exploration on meaningful object extraction integrating reflecting surfaces. Detection of specular reflecting surfaces can be useful in target identification and then can be applied to environmental monitoring, disaster prediction and analysis, military, and counter-terrorism. Our method is based on a statistical model to capture the statistical properties of specular reflecting surfaces. And then the reflecting surfaces are detected through cluster analysis.

Time series expression analyses using RNA-seq: a statistical approach.

PubMed

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

Best practices from WisDOT mega and ARRA projects : statistical analysis and % time vs. % cost metrics.

DOT National Transportation Integrated Search

2012-03-01

This study was undertaken to: 1) apply a benchmarking process to identify best practices within four areas Wisconsin Department of Transportation (WisDOT) construction management and 2) analyze two performance metrics, % Cost vs. % Time, tracked by t...

Risk Driven Outcome-Based Command and Control (C2) Assessment

DTIC Science & Technology

2000-01-01

shaping the risk ranking scores into more interpretable and statistically sound risk measures. Regression analysis was applied to determine what...Architecture Framework Implementation, AFCEA Coursebook 503J, February 8-11, 2000, San Diego, California. [Morgan and Henrion, 1990] M. Granger Morgan and

"Scholarship Reconsidered": Reconsidered

ERIC Educational Resources Information Center

Bowden, Randall G.

2007-01-01

"Scholarship Reconsidered" by Ernest Boyer generates a flurry of theoretical and applied activity. Much of the research centers on the concept of the scholarship of teaching as researchers explore what constitutes scholarship, which is often misdirected. Through lexical statistics and rhetorical analysis, the text is examined according to its…

40 CFR 796.2750 - Sediment and soil adsorption isotherm.

Code of Federal Regulations, 2014 CFR

2014-07-01

... are highly reproducible. The test provides excellent quantitative data readily amenable to statistical... combination of methods suitable for the identification and quantitative detection of the parent test chemical... quantitative analysis of the parent chemical. (3) Amount of parent test chemical applied, the amount recovered...

40 CFR 796.2750 - Sediment and soil adsorption isotherm.

Code of Federal Regulations, 2013 CFR

2013-07-01

... highly reproducible. The test provides excellent quantitative data readily amenable to statistical... combination of methods suitable for the identification and quantitative detection of the parent test chemical... quantitative analysis of the parent chemical. (3) Amount of parent test chemical applied, the amount recovered...

40 CFR 796.2750 - Sediment and soil adsorption isotherm.

Code of Federal Regulations, 2012 CFR

2012-07-01

... highly reproducible. The test provides excellent quantitative data readily amenable to statistical... combination of methods suitable for the identification and quantitative detection of the parent test chemical... quantitative analysis of the parent chemical. (3) Amount of parent test chemical applied, the amount recovered...

Progress in Turbulence Detection via GNSS Occultation Data

NASA Technical Reports Server (NTRS)

Cornman, L. B.; Goodrich, R. K.; Axelrad, P.; Barlow, E.

2012-01-01

The increased availability of radio occultation (RO) data offers the ability to detect and study turbulence in the Earth's atmosphere. An analysis of how RO data can be used to determine the strength and location of turbulent regions is presented. This includes the derivation of a model for the power spectrum of the log-amplitude and phase fluctuations of the permittivity (or index of refraction) field. The bulk of the paper is then concerned with the estimation of the model parameters. Parameter estimators are introduced and some of their statistical properties are studied. These estimators are then applied to simulated log-amplitude RO signals. This includes the analysis of global statistics derived from a large number of realizations, as well as case studies that illustrate various specific aspects of the problem. Improvements to the basic estimation methods are discussed, and their beneficial properties are illustrated. The estimation techniques are then applied to real occultation data. Only two cases are presented, but they illustrate some of the salient features inherent in real data.

A Monte Carlo–Based Bayesian Approach for Measuring Agreement in a Qualitative Scale

PubMed Central

Pérez Sánchez, Carlos Javier

2014-01-01

Agreement analysis has been an active research area whose techniques have been widely applied in psychology and other fields. However, statistical agreement among raters has been mainly considered from a classical statistics point of view. Bayesian methodology is a viable alternative that allows the inclusion of subjective initial information coming from expert opinions, personal judgments, or historical data. A Bayesian approach is proposed by providing a unified Monte Carlo–based framework to estimate all types of measures of agreement in a qualitative scale of response. The approach is conceptually simple and it has a low computational cost. Both informative and non-informative scenarios are considered. In case no initial information is available, the results are in line with the classical methodology, but providing more information on the measures of agreement. For the informative case, some guidelines are presented to elicitate the prior distribution. The approach has been applied to two applications related to schizophrenia diagnosis and sensory analysis. PMID:29881002

Evaluation of the best fit distribution for partial duration series of daily rainfall in Madinah, western Saudi Arabia

NASA Astrophysics Data System (ADS)

Alahmadi, F.; Rahman, N. A.; Abdulrazzak, M.

2014-09-01

Rainfall frequency analysis is an essential tool for the design of water related infrastructure. It can be used to predict future flood magnitudes for a given magnitude and frequency of extreme rainfall events. This study analyses the application of rainfall partial duration series (PDS) in the vast growing urban Madinah city located in the western part of Saudi Arabia. Different statistical distributions were applied (i.e. Normal, Log Normal, Extreme Value type I, Generalized Extreme Value, Pearson Type III, Log Pearson Type III) and their distribution parameters were estimated using L-moments methods. Also, different selection criteria models are applied, e.g. Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and Anderson-Darling Criterion (ADC). The analysis indicated the advantage of Generalized Extreme Value as the best fit statistical distribution for Madinah partial duration daily rainfall series. The outcome of such an evaluation can contribute toward better design criteria for flood management, especially flood protection measures.

Adverse effects of metallic artifacts on voxel-wise analysis and tract-based spatial statistics in diffusion tensor imaging.

PubMed

Goto, Masami; Abe, Osamu; Hata, Junichi; Fukunaga, Issei; Shimoji, Keigo; Kunimatsu, Akira; Gomi, Tsutomu

2017-02-01

Background Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that reflects the Brownian motion of water molecules constrained within brain tissue. Fractional anisotropy (FA) is one of the most commonly measured DTI parameters, and can be applied to quantitative analysis of white matter as tract-based spatial statistics (TBSS) and voxel-wise analysis. Purpose To show an association between metallic implants and the results of statistical analysis (voxel-wise group comparison and TBSS) for fractional anisotropy (FA) mapping, in DTI of healthy adults. Material and Methods Sixteen healthy volunteers were scanned with 3-Tesla MRI. A magnetic keeper type of dental implant was used as the metallic implant. DTI was acquired three times in each participant: (i) without a magnetic keeper (FAnon1); (ii) with a magnetic keeper (FAimp); and (iii) without a magnetic keeper (FAnon2) as reproducibility of FAnon1. Group comparisons with paired t-test were performed as FAnon1 vs. FAnon2, and as FAnon1 vs. FAimp. Results Regions of significantly reduced and increased local FA values were revealed by voxel-wise group comparison analysis (a P value of less than 0.05, corrected with family-wise error), but not by TBSS. Conclusion Metallic implants existing outside the field of view produce artifacts that affect the statistical analysis (voxel-wise group comparisons) for FA mapping. When statistical analysis for FA mapping is conducted by researchers, it is important to pay attention to any dental implants present in the mouths of the participants.

Fisher statistics for analysis of diffusion tensor directional information.

PubMed

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. Copyright © 2012 Elsevier B.V. All rights reserved.

A better way to evaluate remote monitoring programs in chronic disease care: receiver operating characteristic analysis.

PubMed

Brown Connolly, Nancy E

2014-12-01

This foundational study applies the process of receiver operating characteristic (ROC) analysis to evaluate utility and predictive value of a disease management (DM) model that uses RM devices for chronic obstructive pulmonary disease (COPD). The literature identifies a need for a more rigorous method to validate and quantify evidence-based value for remote monitoring (RM) systems being used to monitor persons with a chronic disease. ROC analysis is an engineering approach widely applied in medical testing, but that has not been evaluated for its utility in RM. Classifiers (saturated peripheral oxygen [SPO2], blood pressure [BP], and pulse), optimum threshold, and predictive accuracy are evaluated based on patient outcomes. Parametric and nonparametric methods were used. Event-based patient outcomes included inpatient hospitalization, accident and emergency, and home health visits. Statistical analysis tools included Microsoft (Redmond, WA) Excel(®) and MedCalc(®) (MedCalc Software, Ostend, Belgium) version 12 © 1993-2013 to generate ROC curves and statistics. Persons with COPD were monitored a minimum of 183 days, with at least one inpatient hospitalization within 12 months prior to monitoring. Retrospective, de-identified patient data from a United Kingdom National Health System COPD program were used. Datasets included biometric readings, alerts, and resource utilization. SPO2 was identified as a predictive classifier, with an optimal average threshold setting of 85-86%. BP and pulse were failed classifiers, and areas of design were identified that may improve utility and predictive capacity. Cost avoidance methodology was developed. RESULTS can be applied to health services planning decisions. Methods can be applied to system design and evaluation based on patient outcomes. This study validated the use of ROC in RM program evaluation.

A reduction in ag/residential signature conflict using principal components analysis of LANDSAT temporal data

NASA Technical Reports Server (NTRS)

Williams, D. L.; Borden, F. Y.

1977-01-01

Methods to accurately delineate the types of land cover in the urban-rural transition zone of metropolitan areas were considered. The application of principal components analysis to multidate LANDSAT imagery was investigated as a means of reducing the overlap between residential and agricultural spectral signatures. The statistical concepts of principal components analysis were discussed, as well as the results of this analysis when applied to multidate LANDSAT imagery of the Washington, D.C. metropolitan area.

Visibility graph analysis on heartbeat dynamics of meditation training

NASA Astrophysics Data System (ADS)

Jiang, Sen; Bian, Chunhua; Ning, Xinbao; Ma, Qianli D. Y.

2013-06-01

We apply the visibility graph analysis to human heartbeat dynamics by constructing the complex networks of heartbeat interval time series and investigating the statistical properties of the network before and during chi and yoga meditation. The experiment results show that visibility graph analysis can reveal the dynamical changes caused by meditation training manifested as regular heartbeat, which is closely related to the adjustment of autonomous neural system, and visibility graph analysis is effective to evaluate the effect of meditation.

The effect of normalization of Partial Directed Coherence on the statistical assessment of connectivity patterns: a simulation study.

PubMed

Toppi, J; Petti, M; Vecchiato, G; Cincotti, F; Salinari, S; Mattia, D; Babiloni, F; Astolfi, L

2013-01-01

Partial Directed Coherence (PDC) is a spectral multivariate estimator for effective connectivity, relying on the concept of Granger causality. Even if its original definition derived directly from information theory, two modifies were introduced in order to provide better physiological interpretations of the estimated networks: i) normalization of the estimator according to rows, ii) squared transformation. In the present paper we investigated the effect of PDC normalization on the performances achieved by applying the statistical validation process on investigated connectivity patterns under different conditions of Signal to Noise ratio (SNR) and amount of data available for the analysis. Results of the statistical analysis revealed an effect of PDC normalization only on the percentages of type I and type II errors occurred by using Shuffling procedure for the assessment of connectivity patterns. No effects of the PDC formulation resulted on the performances achieved during the validation process executed instead by means of Asymptotic Statistic approach. Moreover, the percentages of both false positives and false negatives committed by Asymptotic Statistic are always lower than those achieved by Shuffling procedure for each type of normalization.

Covariance approximation for fast and accurate computation of channelized Hotelling observer statistics

NASA Astrophysics Data System (ADS)

Bonetto, P.; Qi, Jinyi; Leahy, R. M.

2000-08-01

Describes a method for computing linear observer statistics for maximum a posteriori (MAP) reconstructions of PET images. The method is based on a theoretical approximation for the mean and covariance of MAP reconstructions. In particular, the authors derive here a closed form for the channelized Hotelling observer (CHO) statistic applied to 2D MAP images. The theoretical analysis models both the Poission statistics of PET data and the inhomogeneity of tracer uptake. The authors show reasonably good correspondence between these theoretical results and Monte Carlo studies. The accuracy and low computational cost of the approximation allow the authors to analyze the observer performance over a wide range of operating conditions and parameter settings for the MAP reconstruction algorithm.

Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: a systematic review.

PubMed

Weir, Christopher J; Butcher, Isabella; Assi, Valentina; Lewis, Stephanie C; Murray, Gordon D; Langhorne, Peter; Brady, Marian C

2018-03-07

Rigorous, informative meta-analyses rely on availability of appropriate summary statistics or individual participant data. For continuous outcomes, especially those with naturally skewed distributions, summary information on the mean or variability often goes unreported. While full reporting of original trial data is the ideal, we sought to identify methods for handling unreported mean or variability summary statistics in meta-analysis. We undertook two systematic literature reviews to identify methodological approaches used to deal with missing mean or variability summary statistics. Five electronic databases were searched, in addition to the Cochrane Colloquium abstract books and the Cochrane Statistics Methods Group mailing list archive. We also conducted cited reference searching and emailed topic experts to identify recent methodological developments. Details recorded included the description of the method, the information required to implement the method, any underlying assumptions and whether the method could be readily applied in standard statistical software. We provided a summary description of the methods identified, illustrating selected methods in example meta-analysis scenarios. For missing standard deviations (SDs), following screening of 503 articles, fifteen methods were identified in addition to those reported in a previous review. These included Bayesian hierarchical modelling at the meta-analysis level; summary statistic level imputation based on observed SD values from other trials in the meta-analysis; a practical approximation based on the range; and algebraic estimation of the SD based on other summary statistics. Following screening of 1124 articles for methods estimating the mean, one approximate Bayesian computation approach and three papers based on alternative summary statistics were identified. Illustrative meta-analyses showed that when replacing a missing SD the approximation using the range minimised loss of precision and generally performed better than omitting trials. When estimating missing means, a formula using the median, lower quartile and upper quartile performed best in preserving the precision of the meta-analysis findings, although in some scenarios, omitting trials gave superior results. Methods based on summary statistics (minimum, maximum, lower quartile, upper quartile, median) reported in the literature facilitate more comprehensive inclusion of randomised controlled trials with missing mean or variability summary statistics within meta-analyses.

Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.

PubMed

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

2015-01-08

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. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

An ANOVA approach for statistical comparisons of brain networks.

PubMed

Fraiman, Daniel; Fraiman, Ricardo

2018-03-16

The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

A method of using cluster analysis to study statistical dependence in multivariate data

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Card, D. H.; Lyle, G. C.

1975-01-01

A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.

The Market Responses to the Government Regulation of Chlorinated Solvents: A Policy Analysis

DTIC Science & Technology

1988-10-01

in the process of statistical estimation of model parameters. The results of the estimation process applied to chlorinated solvent markets show the...93 C.5. Marginal Feedstock Cost Series Estimates for Process Share of Total Production .................................. 94 F.I...poliay context for this research. Section III provides analysis necessary to understand the chemicals involved, their production processes and costs, and

The human chromosomal fragile sites more often involved in constitutional deletions and duplications - A genetic and statistical assessment

NASA Astrophysics Data System (ADS)

Gomes, Dora Prata; Sequeira, Inês J.; Figueiredo, Carlos; Rueff, José; Brás, Aldina

2016-12-01

Human chromosomal fragile sites (CFSs) are heritable loci or regions of the human chromosomes prone to exhibit gaps, breaks and rearrangements. Determining the frequency of deletions and duplications in CFSs may contribute to explain the occurrence of human disease due to those rearrangements. In this study we analyzed the frequency of deletions and duplications in each human CFS. Statistical methods, namely data display, descriptive statistics and linear regression analysis were applied to analyze this dataset. We found that FRA15C, FRA16A and FRAXB are the most frequently involved CFSs in deletions and duplications occurring in the human genome.

Gene Identification Algorithms Using Exploratory Statistical Analysis of Periodicity

NASA Astrophysics Data System (ADS)

Mukherjee, Shashi Bajaj; Sen, Pradip Kumar

2010-10-01

Studying periodic pattern is expected as a standard line of attack for recognizing DNA sequence in identification of gene and similar problems. But peculiarly very little significant work is done in this direction. This paper studies statistical properties of DNA sequences of complete genome using a new technique. A DNA sequence is converted to a numeric sequence using various types of mappings and standard Fourier technique is applied to study the periodicity. Distinct statistical behaviour of periodicity parameters is found in coding and non-coding sequences, which can be used to distinguish between these parts. Here DNA sequences of Drosophila melanogaster were analyzed with significant accuracy.

Parameters optimization defined by statistical analysis for cysteine-dextran radiolabeling with technetium tricarbonyl core.

PubMed

Núñez, Eutimio Gustavo Fernández; Faintuch, Bluma Linkowski; Teodoro, Rodrigo; Wiecek, Danielle Pereira; da Silva, Natanael Gomes; Papadopoulos, Minas; Pelecanou, Maria; Pirmettis, Ioannis; de Oliveira Filho, Renato Santos; Duatti, Adriano; Pasqualini, Roberto

2011-04-01

The objective of this study was the development of a statistical approach for radiolabeling optimization of cysteine-dextran conjugates with Tc-99m tricarbonyl core. This strategy has been applied to the labeling of 2-propylene-S-cysteine-dextran in the attempt to prepare a new class of tracers for sentinel lymph node detection, and can be extended to other radiopharmaceuticals for different targets. The statistical routine was based on three-level factorial design. Best labeling conditions were achieved. The specific activity reached was 5 MBq/μg. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Selecting statistical model and optimum maintenance policy: a case study of hydraulic pump.

PubMed

Ruhi, S; Karim, M R

2016-01-01

Proper maintenance policy can play a vital role for effective investigation of product reliability. Every engineered object such as product, plant or infrastructure needs preventive and corrective maintenance. In this paper we look at a real case study. It deals with the maintenance of hydraulic pumps used in excavators by a mining company. We obtain the data that the owner had collected and carry out an analysis and building models for pump failures. The data consist of both failure and censored lifetimes of the hydraulic pump. Different competitive mixture models are applied to analyze a set of maintenance data of a hydraulic pump. Various characteristics of the mixture models, such as the cumulative distribution function, reliability function, mean time to failure, etc. are estimated to assess the reliability of the pump. Akaike Information Criterion, adjusted Anderson-Darling test statistic, Kolmogrov-Smirnov test statistic and root mean square error are considered to select the suitable models among a set of competitive models. The maximum likelihood estimation method via the EM algorithm is applied mainly for estimating the parameters of the models and reliability related quantities. In this study, it is found that a threefold mixture model (Weibull-Normal-Exponential) fits well for the hydraulic pump failures data set. This paper also illustrates how a suitable statistical model can be applied to estimate the optimum maintenance period at a minimum cost of a hydraulic pump.

Toward improved analysis of concentration data: Embracing nondetects.

PubMed

Shoari, Niloofar; Dubé, Jean-Sébastien

2018-03-01

Various statistical tests on concentration data serve to support decision-making regarding characterization and monitoring of contaminated media, assessing exposure to a chemical, and quantifying the associated risks. However, the routine statistical protocols cannot be directly applied because of challenges arising from nondetects or left-censored observations, which are concentration measurements below the detection limit of measuring instruments. Despite the existence of techniques based on survival analysis that can adjust for nondetects, these are seldom taken into account properly. A comprehensive review of the literature showed that managing policies regarding analysis of censored data do not always agree and that guidance from regulatory agencies may be outdated. Therefore, researchers and practitioners commonly resort to the most convenient way of tackling the censored data problem by substituting nondetects with arbitrary constants prior to data analysis, although this is generally regarded as a bias-prone approach. Hoping to improve the interpretation of concentration data, the present article aims to familiarize researchers in different disciplines with the significance of left-censored observations and provides theoretical and computational recommendations (under both frequentist and Bayesian frameworks) for adequate analysis of censored data. In particular, the present article synthesizes key findings from previous research with respect to 3 noteworthy aspects of inferential statistics: estimation of descriptive statistics, hypothesis testing, and regression analysis. Environ Toxicol Chem 2018;37:643-656. © 2017 SETAC. © 2017 SETAC.

A random-sum Wilcoxon statistic and its application to analysis of ROC and LROC data.

PubMed

Tang, Liansheng Larry; Balakrishnan, N

2011-01-01

The Wilcoxon-Mann-Whitney statistic is commonly used for a distribution-free comparison of two groups. One requirement for its use is that the sample sizes of the two groups are fixed. This is violated in some of the applications such as medical imaging studies and diagnostic marker studies; in the former, the violation occurs since the number of correctly localized abnormal images is random, while in the latter the violation is due to some subjects not having observable measurements. For this reason, we propose here a random-sum Wilcoxon statistic for comparing two groups in the presence of ties, and derive its variance as well as its asymptotic distribution for large sample sizes. The proposed statistic includes the regular Wilcoxon rank-sum statistic. Finally, we apply the proposed statistic for summarizing location response operating characteristic data from a liver computed tomography study, and also for summarizing diagnostic accuracy of biomarker data.

[The application of the multidimensional statistical methods in the evaluation of the influence of atmospheric pollution on the population's health].

PubMed

Surzhikov, V D; Surzhikov, D V

2014-01-01

The search and measurement of causal relationships between exposure to air pollution and health state of the population is based on the system analysis and risk assessment to improve the quality of research. With this purpose there is applied the modern statistical analysis with the use of criteria of independence, principal component analysis and discriminate function analysis. As a result of analysis out of all atmospheric pollutants there were separated four main components: for diseases of the circulatory system main principal component is implied with concentrations of suspended solids, nitrogen dioxide, carbon monoxide, hydrogen fluoride, for the respiratory diseases the main c principal component is closely associated with suspended solids, sulfur dioxide and nitrogen dioxide, charcoal black. The discriminant function was shown to be used as a measure of the level of air pollution.

A Finite-Volume "Shaving" Method for Interfacing NASA/DAO''s Physical Space Statistical Analysis System to the Finite-Volume GCM with a Lagrangian Control-Volume Vertical Coordinate

NASA Technical Reports Server (NTRS)

Lin, Shian-Jiann; DaSilva, Arlindo; Atlas, Robert (Technical Monitor)

2001-01-01

Toward the development of a finite-volume Data Assimilation System (fvDAS), a consistent finite-volume methodology is developed for interfacing the NASA/DAO's Physical Space Statistical Analysis System (PSAS) to the joint NASA/NCAR finite volume CCM3 (fvCCM3). To take advantage of the Lagrangian control-volume vertical coordinate of the fvCCM3, a novel "shaving" method is applied to the lowest few model layers to reflect the surface pressure changes as implied by the final analysis. Analysis increments (from PSAS) to the upper air variables are then consistently put onto the Lagrangian layers as adjustments to the volume-mean quantities during the analysis cycle. This approach is demonstrated to be superior to the conventional method of using independently computed "tendency terms" for surface pressure and upper air prognostic variables.

Rapid analysis of pharmaceutical drugs using LIBS coupled with multivariate analysis.

PubMed

Tiwari, P K; Awasthi, S; Kumar, R; Anand, R K; Rai, P K; Rai, A K

2018-02-01

Type 2 diabetes drug tablets containing voglibose having dose strengths of 0.2 and 0.3 mg of various brands have been examined, using laser-induced breakdown spectroscopy (LIBS) technique. The statistical methods such as the principal component analysis (PCA) and the partial least square regression analysis (PLSR) have been employed on LIBS spectral data for classifying and developing the calibration models of drug samples. We have developed the ratio-based calibration model applying PLSR in which relative spectral intensity ratios H/C, H/N and O/N are used. Further, the developed model has been employed to predict the relative concentration of element in unknown drug samples. The experiment has been performed in air and argon atmosphere, respectively, and the obtained results have been compared. The present model provides rapid spectroscopic method for drug analysis with high statistical significance for online control and measurement process in a wide variety of pharmaceutical industrial applications.

A global goodness-of-fit statistic for Cox regression models.

PubMed

Parzen, M; Lipsitz, S R

1999-06-01

In this paper, a global goodness-of-fit test statistic for a Cox regression model, which has an approximate chi-squared distribution when the model has been correctly specified, is proposed. Our goodness-of-fit statistic is global and has power to detect if interactions or higher order powers of covariates in the model are needed. The proposed statistic is similar to the Hosmer and Lemeshow (1980, Communications in Statistics A10, 1043-1069) goodness-of-fit statistic for binary data as well as Schoenfeld's (1980, Biometrika 67, 145-153) statistic for the Cox model. The methods are illustrated using data from a Mayo Clinic trial in primary billiary cirrhosis of the liver (Fleming and Harrington, 1991, Counting Processes and Survival Analysis), in which the outcome is the time until liver transplantation or death. The are 17 possible covariates. Two Cox proportional hazards models are fit to the data, and the proposed goodness-of-fit statistic is applied to the fitted models.

Quantifying the Energy Landscape Statistics in Proteins - a Relaxation Mode Analysis

NASA Astrophysics Data System (ADS)

Cai, Zhikun; Zhang, Yang

Energy landscape, the hypersurface in the configurational space, has been a useful concept in describing complex processes that occur over a very long time scale, such as the multistep slow relaxations of supercooled liquids and folding of polypeptide chains into structured proteins. Despite extensive simulation studies, its experimental characterization still remains a challenge. To address this challenge, we developed a relaxation mode analysis (RMA) for liquids under a framework analogous to the normal mode analysis for solids. Using RMA, important statistics of the activation barriers of the energy landscape becomes accessible from experimentally measurable two-point correlation functions, e.g. using quasi-elastic and inelastic scattering experiments. We observed a prominent coarsening effect of the energy landscape. The results were further confirmed by direct sampling of the energy landscape using a metadynamics-like adaptive autonomous basin climbing computation. We first demonstrate RMA in a supercooled liquid when dynamical cooperativity emerges in the landscape-influenced regime. Then we show this framework reveals encouraging energy landscape statistics when applied to proteins.

Automated three-dimensional quantification of myocardial perfusion and brain SPECT.

PubMed

Slomka, P J; Radau, P; Hurwitz, G A; Dey, D

2001-01-01

To allow automated and objective reading of nuclear medicine tomography, we have developed a set of tools for clinical analysis of myocardial perfusion tomography (PERFIT) and Brain SPECT/PET (BRASS). We exploit algorithms for image registration and use three-dimensional (3D) "normal models" for individual patient comparisons to composite datasets on a "voxel-by-voxel basis" in order to automatically determine the statistically significant abnormalities. A multistage, 3D iterative inter-subject registration of patient images to normal templates is applied, including automated masking of the external activity before final fit. In separate projects, the software has been applied to the analysis of myocardial perfusion SPECT, as well as brain SPECT and PET data. Automatic reading was consistent with visual analysis; it can be applied to the whole spectrum of clinical images, and aid physicians in the daily interpretation of tomographic nuclear medicine images.

10 CFR 431.17 - Determination of efficiency.

Code of Federal Regulations, 2014 CFR

2014-01-01

... characteristics of that basic model, and (ii) Based on engineering or statistical analysis, computer simulation or... simulation or modeling, and other analytic evaluation of performance data on which the AEDM is based... applied. (iii) If requested by the Department, the manufacturer shall conduct simulations to predict the...

10 CFR 431.17 - Determination of efficiency.

Code of Federal Regulations, 2012 CFR

2012-01-01

... characteristics of that basic model, and (ii) Based on engineering or statistical analysis, computer simulation or... simulation or modeling, and other analytic evaluation of performance data on which the AEDM is based... applied. (iii) If requested by the Department, the manufacturer shall conduct simulations to predict the...

Elevation, rootstock, and soil depth affect the nutritional quality of mandarin oranges

USDA-ARS?s Scientific Manuscript database

The effects of elevation, rootstock, and soil depth on the nutritional quality of mandarin oranges from 11 groves in California were investigated by nuclear magnetic resonance (NMR) spectroscopy by quantifying 29 compounds and applying multivariate statistical data analysis. A comparison of the juic...

The Stratification Analysis of Sediment Data for Lake Michigan

EPA Science Inventory

This research paper describes the development of spatial statistical tools that are applied to investigate the spatial trends of sediment data sets for nutrients and carbon in Lake Michigan. All of the sediment data utilized in the present study was collected over a two year per...

Efficacy and safety of local steroids for urethra strictures: a systematic review and meta-analysis.

PubMed

Zhang, Kaile; Qi, Er; Zhang, Yumeng; Sa, Yinglong; Fu, Qiang

2014-08-01

Local steroids have been used as an adjuvant therapy to patients undergoing internal urethrotomy (IU) in treating urethral strictures. Whether this technique is effective and safe is still controversial. The aim of this study is to determine the efficacy and safety of local steroids as applied with the IU procedure. A systematic review of the literature was performed by searching Medline, Embase, Cochrane Library Databases, and the Web of Science. We included only prospective randomized, controlled trials that compared the efficacy and safety between IU procedures with applied local steroids and those without. Eight studies were found eligible for further analysis. In total, 203 patients undergoing IU were treated with steroid injection or catheter lubrication. Time to recurrence is statistically significant (mean: 10.14 and 5.07 months, P<0.00001).The number of patients with recurrent stricture formation significantly decreased at different follow-up time points (P=0.05).No statistically significant differences were found between the recurrence rates, adverse effects, and success rates of second IUs in patients with applied local steroids and those without. The use of local steroids with IU seems to prolong time to stricture recurrence but does not seem to affect the high stricture recurrence rate following IU. When local steroids are applied with complementary intention, the disease control outcomes are encouraging. Further robust comparative effectiveness studies are now required.

Statistical Literacy among Applied Linguists and Second Language Acquisition Researchers

ERIC Educational Resources Information Center

Loewen, Shawn; Lavolette, Elizabeth; Spino, Le Anne; Papi, Mostafa; Schmidtke, Jens; Sterling, Scott; Wolff, Dominik

2014-01-01

The importance of statistical knowledge in applied linguistics and second language acquisition (SLA) research has been emphasized in recent publications. However, the last investigation of the statistical literacy of applied linguists occurred more than 25 years ago (Lazaraton, Riggenbach, & Ediger, 1987). The current study undertook a partial…

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Statistical Model Selection for TID Hardness Assurance

NASA Technical Reports Server (NTRS)

Ladbury, R.; Gorelick, J. L.; McClure, S.

2010-01-01

Radiation Hardness Assurance (RHA) methodologies against Total Ionizing Dose (TID) degradation impose rigorous statistical treatments for data from a part's Radiation Lot Acceptance Test (RLAT) and/or its historical performance. However, no similar methods exist for using "similarity" data - that is, data for similar parts fabricated in the same process as the part under qualification. This is despite the greater difficulty and potential risk in interpreting of similarity data. In this work, we develop methods to disentangle part-to-part, lot-to-lot and part-type-to-part-type variation. The methods we develop apply not just for qualification decisions, but also for quality control and detection of process changes and other "out-of-family" behavior. We begin by discussing the data used in ·the study and the challenges of developing a statistic providing a meaningful measure of degradation across multiple part types, each with its own performance specifications. We then develop analysis techniques and apply them to the different data sets.

Assessment of variations in thermal cycle life data of thermal barrier coated rods

NASA Astrophysics Data System (ADS)

Hendricks, R. C.; McDonald, G.

An analysis of thermal cycle life data for 22 thermal barrier coated (TBC) specimens was conducted. The Zr02-8Y203/NiCrAlY plasma spray coated Rene 41 rods were tested in a Mach 0.3 Jet A/air burner flame. All specimens were subjected to the same coating and subsequent test procedures in an effort to control three parametric groups; material properties, geometry and heat flux. Statistically, the data sample space had a mean of 1330 cycles with a standard deviation of 520 cycles. The data were described by normal or log-normal distributions, but other models could also apply; the sample size must be increased to clearly delineate a statistical failure model. The statistical methods were also applied to adhesive/cohesive strength data for 20 TBC discs of the same composition, with similar results. The sample space had a mean of 9 MPa with a standard deviation of 4.2 MPa.

Assessment of variations in thermal cycle life data of thermal barrier coated rods

NASA Technical Reports Server (NTRS)

Hendricks, R. C.; Mcdonald, G.

1981-01-01

An analysis of thermal cycle life data for 22 thermal barrier coated (TBC) specimens was conducted. The Zr02-8Y203/NiCrAlY plasma spray coated Rene 41 rods were tested in a Mach 0.3 Jet A/air burner flame. All specimens were subjected to the same coating and subsequent test procedures in an effort to control three parametric groups; material properties, geometry and heat flux. Statistically, the data sample space had a mean of 1330 cycles with a standard deviation of 520 cycles. The data were described by normal or log-normal distributions, but other models could also apply; the sample size must be increased to clearly delineate a statistical failure model. The statistical methods were also applied to adhesive/cohesive strength data for 20 TBC discs of the same composition, with similar results. The sample space had a mean of 9 MPa with a standard deviation of 4.2 MPa.

Application of image recognition algorithms for statistical description of nano- and microstructured surfaces

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

Mărăscu, V.; Dinescu, G.; Faculty of Physics, University of Bucharest, 405 Atomistilor Street, Bucharest-Magurele

In this paper we propose a statistical approach for describing the self-assembling of sub-micronic polystyrene beads on silicon surfaces, as well as the evolution of surface topography due to plasma treatments. Algorithms for image recognition are used in conjunction with Scanning Electron Microscopy (SEM) imaging of surfaces. In a first step, greyscale images of the surface covered by the polystyrene beads are obtained. Further, an adaptive thresholding method was applied for obtaining binary images. The next step consisted in automatic identification of polystyrene beads dimensions, by using Hough transform algorithm, according to beads radius. In order to analyze the uniformitymore » of the self–assembled polystyrene beads, the squared modulus of 2-dimensional Fast Fourier Transform (2- D FFT) was applied. By combining these algorithms we obtain a powerful and fast statistical tool for analysis of micro and nanomaterials with aspect features regularly distributed on surface upon SEM examination.« less

A Novel Genome-Information Content-Based Statistic for Genome-Wide Association Analysis Designed for Next-Generation Sequencing Data

PubMed Central

Luo, Li; Zhu, Yun

2012-01-01

Abstract The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T2, collapsing method, multivariate and collapsing (CMC) method, individual χ2 test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets. PMID:22651812

A novel genome-information content-based statistic for genome-wide association analysis designed for next-generation sequencing data.

PubMed

Luo, Li; Zhu, Yun; Xiong, Momiao

2012-06-01

The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T(2), collapsing method, multivariate and collapsing (CMC) method, individual χ(2) test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets.

A Data Assimilation System For Operational Weather Forecast In Galicia Region (nw Spain)

NASA Astrophysics Data System (ADS)

Balseiro, C. F.; Souto, M. J.; Pérez-Muñuzuri, V.; Brewster, K.; Xue, M.

Regional weather forecast models, such as the Advanced Regional Prediction System (ARPS), over complex environments with varying local influences require an accurate meteorological analysis that should include all local meteorological measurements available. In this work, the ARPS Data Analysis System (ADAS) (Xue et al. 2001) is applied as a three-dimensional weather analysis tool to include surface station and rawinsonde data with the NCEP AVN forecasts as the analysis background. Currently in ADAS, a set of five meteorological variables are considered during the analysis: horizontal grid-relative wind components, pressure, potential temperature and spe- cific humidity. The analysis is used for high resolution numerical weather prediction for the Galicia region. The analysis method used in ADAS is based on the successive corrective scheme of Bratseth (1986), which asymptotically approaches the result of a statistical (optimal) interpolation, but at lower computational cost. As in the optimal interpolation scheme, the Bratseth interpolation method can take into account the rel- ative error between background and observational data, therefore they are relatively insensitive to large variations in data density and can integrate data of mixed accuracy. This method can be applied economically in an operational setting, providing signifi- cant improvement over the background model forecast as well as any analysis without high-resolution local observations. A one-way nesting is applied for weather forecast in Galicia region, and the use of this assimilation system in both domains shows better results not only in initial conditions but also in all forecast periods. Bratseth, A.M. (1986): "Statistical interpolation by means of successive corrections." Tellus, 38A, 439-447. Souto, M. J., Balseiro, C. F., Pérez-Muñuzuri, V., Xue, M. Brewster, K., (2001): "Im- pact of cloud analysis on numerical weather prediction in the galician region of Spain". Submitted to Journal of Applied Meteorology. Xue, M., Wang. D., Gao, J., Brewster, K, Droegemeier, K. K., (2001): "The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation". Meteor. Atmos Physics. Accepted

Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs

PubMed Central

Asher, Lucy; Harvey, Naomi D.; Green, Martin; England, Gary C. W.

2017-01-01

Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that completed training and qualified as a guide dog, to those that were withdrawn from the training program. Survival analysis allows the time to (or between) a binary event(s) and the probability of the event occurring at or beyond a specified time point. Survival analysis, using a Cox proportional hazards model, was used to examine the time taken to withdraw a dog from training. Sex, breed, and other factors affected time to withdrawal. Bitches were withdrawn faster than dogs, Labradors were withdrawn faster, and Labrador × Golden Retrievers slower, than Golden Retriever × Labradors; and dogs not bred by Guide Dogs were withdrawn faster than those bred by Guide Dogs. Multistate modeling (MSM) can be used as an extension of survival analysis to incorporate more than two discrete events or states. Multistate models were used to investigate transitions between states of training to qualification as a guide dog or behavioral withdrawal, and from qualification as a guide dog to behavioral withdrawal. Sex, breed (with purebred Labradors and Golden retrievers differing from F1 crosses), and bred by Guide Dogs or not, effected movements between states. We postulate that survival analysis and MSM could be applied to a wide range of behavioral data and key examples are provided. PMID:28804710

Identifiability of PBPK Models with Applications to ...

EPA Pesticide Factsheets

Any statistical model should be identifiable in order for estimates and tests using it to be meaningful. We consider statistical analysis of physiologically-based pharmacokinetic (PBPK) models in which parameters cannot be estimated precisely from available data, and discuss different types of identifiability that occur in PBPK models and give reasons why they occur. We particularly focus on how the mathematical structure of a PBPK model and lack of appropriate data can lead to statistical models in which it is impossible to estimate at least some parameters precisely. Methods are reviewed which can determine whether a purely linear PBPK model is globally identifiable. We propose a theorem which determines when identifiability at a set of finite and specific values of the mathematical PBPK model (global discrete identifiability) implies identifiability of the statistical model. However, we are unable to establish conditions that imply global discrete identifiability, and conclude that the only safe approach to analysis of PBPK models involves Bayesian analysis with truncated priors. Finally, computational issues regarding posterior simulations of PBPK models are discussed. The methodology is very general and can be applied to numerous PBPK models which can be expressed as linear time-invariant systems. A real data set of a PBPK model for exposure to dimethyl arsinic acid (DMA(V)) is presented to illustrate the proposed methodology. We consider statistical analy

Research on the raw data processing method of the hydropower construction project

NASA Astrophysics Data System (ADS)

Tian, Zhichao

2018-01-01

In this paper, based on the characteristics of the fixed data, this paper compares the various mathematical statistics analysis methods and chooses the improved Grabs criterion to analyze the data, and through the analysis of the data processing, the data processing method is not suitable. It is proved that this method can be applied to the processing of fixed raw data. This paper provides a reference for reasonably determining the effective quota analysis data.

Statistical issues in quality control of proteomic analyses: good experimental design and planning.

PubMed

Cairns, David A

2011-03-01

Quality control is becoming increasingly important in proteomic investigations as experiments become more multivariate and quantitative. Quality control applies to all stages of an investigation and statistics can play a key role. In this review, the role of statistical ideas in the design and planning of an investigation is described. This involves the design of unbiased experiments using key concepts from statistical experimental design, the understanding of the biological and analytical variation in a system using variance components analysis and the determination of a required sample size to perform a statistically powerful investigation. These concepts are described through simple examples and an example data set from a 2-D DIGE pilot experiment. Each of these concepts can prove useful in producing better and more reproducible data. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Tips and Tricks for Successful Application of Statistical Methods to Biological Data.

PubMed

Schlenker, Evelyn

2016-01-01

This chapter discusses experimental design and use of statistics to describe characteristics of data (descriptive statistics) and inferential statistics that test the hypothesis posed by the investigator. Inferential statistics, based on probability distributions, depend upon the type and distribution of the data. For data that are continuous, randomly and independently selected, as well as normally distributed more powerful parametric tests such as Student's t test and analysis of variance (ANOVA) can be used. For non-normally distributed or skewed data, transformation of the data (using logarithms) may normalize the data allowing use of parametric tests. Alternatively, with skewed data nonparametric tests can be utilized, some of which rely on data that are ranked prior to statistical analysis. Experimental designs and analyses need to balance between committing type 1 errors (false positives) and type 2 errors (false negatives). For a variety of clinical studies that determine risk or benefit, relative risk ratios (random clinical trials and cohort studies) or odds ratios (case-control studies) are utilized. Although both use 2 × 2 tables, their premise and calculations differ. Finally, special statistical methods are applied to microarray and proteomics data, since the large number of genes or proteins evaluated increase the likelihood of false discoveries. Additional studies in separate samples are used to verify microarray and proteomic data. Examples in this chapter and references are available to help continued investigation of experimental designs and appropriate data analysis.

Comparative study on the selectivity of various spectrophotometric techniques for the determination of binary mixture of fenbendazole and rafoxanide.

PubMed

Saad, Ahmed S; Attia, Ali K; Alaraki, Manal S; Elzanfaly, Eman S

2015-11-05

Five different spectrophotometric methods were applied for simultaneous determination of fenbendazole and rafoxanide in their binary mixture; namely first derivative, derivative ratio, ratio difference, dual wavelength and H-point standard addition spectrophotometric methods. Different factors affecting each of the applied spectrophotometric methods were studied and the selectivity of the applied methods was compared. The applied methods were validated as per the ICH guidelines and good accuracy; specificity and precision were proven within the concentration range of 5-50 μg/mL for both drugs. Statistical analysis using one-way ANOVA proved no significant differences among the proposed methods for the determination of the two drugs. The proposed methods successfully determined both drugs in laboratory prepared and commercially available binary mixtures, and were found applicable for the routine analysis in quality control laboratories. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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.

Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial

NASA Astrophysics Data System (ADS)

Jahani, Nariman; Cohen, Eric; Hsieh, Meng-Kang; Weinstein, Susan P.; Pantalone, Lauren; Davatzikos, Christos; Kontos, Despina

2018-02-01

We examined the ability of DCE-MRI longitudinal features to give early prediction of recurrence-free survival (RFS) in women undergoing neoadjuvant chemotherapy for breast cancer, in a retrospective analysis of 106 women from the ISPY 1 cohort. These features were based on the voxel-wise changes seen in registered images taken before treatment and after the first round of chemotherapy. We computed the transformation field using a robust deformable image registration technique to match breast images from these two visits. Using the deformation field, parametric response maps (PRM) — a voxel-based feature analysis of longitudinal changes in images between visits — was computed for maps of four kinetic features (signal enhancement ratio, peak enhancement, and wash-in/wash-out slopes). A two-level discrete wavelet transform was applied to these PRMs to extract heterogeneity information about tumor change between visits. To estimate survival, a Cox proportional hazard model was applied with the C statistic as the measure of success in predicting RFS. The best PRM feature (as determined by C statistic in univariable analysis) was determined for each of the four kinetic features. The baseline model, incorporating functional tumor volume, age, race, and hormone response status, had a C statistic of 0.70 in predicting RFS. The model augmented with the four PRM features had a C statistic of 0.76. Thus, our results suggest that adding information on the texture of voxel-level changes in tumor kinetic response between registered images of first and second visits could improve early RFS prediction in breast cancer after neoadjuvant chemotherapy.

Anticoagulant vs. antiplatelet therapy in patients with cryptogenic stroke and patent foramen ovale: an individual participant data meta-analysis.

PubMed

Kent, David M; Dahabreh, Issa J; Ruthazer, Robin; Furlan, Anthony J; Weimar, Christian; Serena, Joaquín; Meier, Bernhard; Mattle, Heinrich P; Di Angelantonio, Emanuele; Paciaroni, Maurizio; Schuchlenz, Herwig; Homma, Shunichi; Lutz, Jennifer S; Thaler, David E

2015-09-14

The preferred antithrombotic strategy for secondary prevention in patients with cryptogenic stroke (CS) and patent foramen ovale (PFO) is unknown. We pooled multiple observational studies and used propensity score-based methods to estimate the comparative effectiveness of oral anticoagulation (OAC) compared with antiplatelet therapy (APT). Individual participant data from 12 databases of medically treated patients with CS and PFO were analysed with Cox regression models, to estimate database-specific hazard ratios (HRs) comparing OAC with APT, for both the primary composite outcome [recurrent stroke, transient ischaemic attack (TIA), or death] and stroke alone. Propensity scores were applied via inverse probability of treatment weighting to control for confounding. We synthesized database-specific HRs using random-effects meta-analysis models. This analysis included 2385 (OAC = 804 and APT = 1581) patients with 227 composite endpoints (stroke/TIA/death). The difference between OAC and APT was not statistically significant for the primary composite outcome [adjusted HR = 0.76, 95% confidence interval (CI) 0.52-1.12] or for the secondary outcome of stroke alone (adjusted HR = 0.75, 95% CI 0.44-1.27). Results were consistent in analyses applying alternative weighting schemes, with the exception that OAC had a statistically significant beneficial effect on the composite outcome in analyses standardized to the patient population who actually received APT (adjusted HR = 0.64, 95% CI 0.42-0.99). Subgroup analyses did not detect statistically significant heterogeneity of treatment effects across clinically important patient groups. We did not find a statistically significant difference comparing OAC with APT; our results justify randomized trials comparing different antithrombotic approaches in these patients. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2015. For permissions please email: journals.permissions@oup.com.

Detecting most influencing courses on students grades using block PCA

NASA Astrophysics Data System (ADS)

Othman, Osama H.; Gebril, Rami Salah

2014-12-01

One of the modern solutions adopted in dealing with the problem of large number of variables in statistical analyses is the Block Principal Component Analysis (Block PCA). This modified technique can be used to reduce the vertical dimension (variables) of the data matrix Xn×p by selecting a smaller number of variables, (say m) containing most of the statistical information. These selected variables can then be employed in further investigations and analyses. Block PCA is an adapted multistage technique of the original PCA. It involves the application of Cluster Analysis (CA) and variable selection throughout sub principal components scores (PC's). The application of Block PCA in this paper is a modified version of the original work of Liu et al (2002). The main objective was to apply PCA on each group of variables, (established using cluster analysis), instead of involving the whole large pack of variables which was proved to be unreliable. In this work, the Block PCA is used to reduce the size of a huge data matrix ((n = 41) × (p = 251)) consisting of Grade Point Average (GPA) of the students in 251 courses (variables) in the faculty of science in Benghazi University. In other words, we are constructing a smaller analytical data matrix of the GPA's of the students with less variables containing most variation (statistical information) in the original database. By applying the Block PCA, (12) courses were found to `absorb' most of the variation or influence from the original data matrix, and hence worth to be keep for future statistical exploring and analytical studies. In addition, the course Independent Study (Math.) was found to be the most influencing course on students GPA among the 12 selected courses.

A comparison between EEG source localization and fMRI during the processing of emotional visual stimuli

NASA Astrophysics Data System (ADS)

Hu, Jin; Tian, Jie; Pan, Xiaohong; Liu, Jiangang

2007-03-01

The purpose of this paper is to compare between EEG source localization and fMRI during emotional processing. 108 pictures for EEG (categorized as positive, negative and neutral) and 72 pictures for fMRI were presented to 24 healthy, right-handed subjects. The fMRI data were analyzed using statistical parametric mapping with SPM2. LORETA was applied to grand averaged ERP data to localize intracranial sources. Statistical analysis was implemented to compare spatiotemporal activation of fMRI and EEG. The fMRI results are in accordance with EEG source localization to some extent, while part of mismatch in localization between the two methods was also observed. In the future we should apply the method for simultaneous recording of EEG and fMRI to our study.

VizieR Online Data Catalog: HARPS timeseries data for HD41248 (Jenkins+, 2014)

NASA Astrophysics Data System (ADS)

Jenkins, J. S.; Tuomi, M.

2017-05-01

We modeled the HARPS radial velocities of HD 42148 by adopting the analysis techniques and the statistical model applied in Tuomi et al. (2014, arXiv:1405.2016). This model contains Keplerian signals, a linear trend, a moving average component with exponential smoothing, and linear correlations with activity indices, namely, BIS, FWHM, and chromospheric activity S index. We applied our statistical model outlined above to the full data set of radial velocities for HD 41248, combining the previously published data in Jenkins et al. (2013ApJ...771...41J) with the newly published data in Santos et al. (2014, J/A+A/566/A35), giving rise to a total time series of 223 HARPS (Mayor et al. 2003Msngr.114...20M) velocities. (1 data file).

Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes

NASA Astrophysics Data System (ADS)

Morozov, Yu. V.; Spektor, A. A.

2017-11-01

A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

Statistical methods for astronomical data with upper limits. II - Correlation and regression

NASA Technical Reports Server (NTRS)

Isobe, T.; Feigelson, E. D.; Nelson, P. I.

1986-01-01

Statistical methods for calculating correlations and regressions in bivariate censored data where the dependent variable can have upper or lower limits are presented. Cox's regression and the generalization of Kendall's rank correlation coefficient provide significant levels of correlations, and the EM algorithm, under the assumption of normally distributed errors, and its nonparametric analog using the Kaplan-Meier estimator, give estimates for the slope of a regression line. Monte Carlo simulations demonstrate that survival analysis is reliable in determining correlations between luminosities at different bands. Survival analysis is applied to CO emission in infrared galaxies, X-ray emission in radio galaxies, H-alpha emission in cooling cluster cores, and radio emission in Seyfert galaxies.

Statistical Analysis for Collision-free Boson Sampling.

PubMed

Huang, He-Liang; Zhong, Han-Sen; Li, Tan; Li, Feng-Guang; Fu, Xiang-Qun; Zhang, Shuo; Wang, Xiang; Bao, Wan-Su

2017-11-10

Boson sampling is strongly believed to be intractable for classical computers but solvable with photons in linear optics, which raises widespread concern as a rapid way to demonstrate the quantum supremacy. However, due to its solution is mathematically unverifiable, how to certify the experimental results becomes a major difficulty in the boson sampling experiment. Here, we develop a statistical analysis scheme to experimentally certify the collision-free boson sampling. Numerical simulations are performed to show the feasibility and practicability of our scheme, and the effects of realistic experimental conditions are also considered, demonstrating that our proposed scheme is experimentally friendly. Moreover, our broad approach is expected to be generally applied to investigate multi-particle coherent dynamics beyond the boson sampling.

Trends in study design and the statistical methods employed in a leading general medicine journal.

PubMed

Gosho, M; Sato, Y; Nagashima, K; Takahashi, S

2018-02-01

Study design and statistical methods have become core components of medical research, and the methodology has become more multifaceted and complicated over time. The study of the comprehensive details and current trends of study design and statistical methods is required to support the future implementation of well-planned clinical studies providing information about evidence-based medicine. Our purpose was to illustrate study design and statistical methods employed in recent medical literature. This was an extension study of Sato et al. (N Engl J Med 2017; 376: 1086-1087), which reviewed 238 articles published in 2015 in the New England Journal of Medicine (NEJM) and briefly summarized the statistical methods employed in NEJM. Using the same database, we performed a new investigation of the detailed trends in study design and individual statistical methods that were not reported in the Sato study. Due to the CONSORT statement, prespecification and justification of sample size are obligatory in planning intervention studies. Although standard survival methods (eg Kaplan-Meier estimator and Cox regression model) were most frequently applied, the Gray test and Fine-Gray proportional hazard model for considering competing risks were sometimes used for a more valid statistical inference. With respect to handling missing data, model-based methods, which are valid for missing-at-random data, were more frequently used than single imputation methods. These methods are not recommended as a primary analysis, but they have been applied in many clinical trials. Group sequential design with interim analyses was one of the standard designs, and novel design, such as adaptive dose selection and sample size re-estimation, was sometimes employed in NEJM. Model-based approaches for handling missing data should replace single imputation methods for primary analysis in the light of the information found in some publications. Use of adaptive design with interim analyses is increasing after the presentation of the FDA guidance for adaptive design. © 2017 John Wiley & Sons Ltd.

Forecasts of non-Gaussian parameter spaces using Box-Cox transformations

NASA Astrophysics Data System (ADS)

Joachimi, B.; Taylor, A. N.

2011-09-01

Forecasts of statistical constraints on model parameters using the Fisher matrix abound in many fields of astrophysics. The Fisher matrix formalism involves the assumption of Gaussianity in parameter space and hence fails to predict complex features of posterior probability distributions. Combining the standard Fisher matrix with Box-Cox transformations, we propose a novel method that accurately predicts arbitrary posterior shapes. The Box-Cox transformations are applied to parameter space to render it approximately multivariate Gaussian, performing the Fisher matrix calculation on the transformed parameters. We demonstrate that, after the Box-Cox parameters have been determined from an initial likelihood evaluation, the method correctly predicts changes in the posterior when varying various parameters of the experimental setup and the data analysis, with marginally higher computational cost than a standard Fisher matrix calculation. We apply the Box-Cox-Fisher formalism to forecast cosmological parameter constraints by future weak gravitational lensing surveys. The characteristic non-linear degeneracy between matter density parameter and normalization of matter density fluctuations is reproduced for several cases, and the capabilities of breaking this degeneracy by weak-lensing three-point statistics is investigated. Possible applications of Box-Cox transformations of posterior distributions are discussed, including the prospects for performing statistical data analysis steps in the transformed Gaussianized parameter space.

Experimental Investigations of Non-Stationary Properties In Radiometer Receivers Using Measurements of Multiple Calibration References

NASA Technical Reports Server (NTRS)

Racette, Paul; Lang, Roger; Zhang, Zhao-Nan; Zacharias, David; Krebs, Carolyn A. (Technical Monitor)

2002-01-01

Radiometers must be periodically calibrated because the receiver response fluctuates. Many techniques exist to correct for the time varying response of a radiometer receiver. An analytical technique has been developed that uses generalized least squares regression (LSR) to predict the performance of a wide variety of calibration algorithms. The total measurement uncertainty including the uncertainty of the calibration can be computed using LSR. The uncertainties of the calibration samples used in the regression are based upon treating the receiver fluctuations as non-stationary processes. Signals originating from the different sources of emission are treated as simultaneously existing random processes. Thus, the radiometer output is a series of samples obtained from these random processes. The samples are treated as random variables but because the underlying processes are non-stationary the statistics of the samples are treated as non-stationary. The statistics of the calibration samples depend upon the time for which the samples are to be applied. The statistics of the random variables are equated to the mean statistics of the non-stationary processes over the interval defined by the time of calibration sample and when it is applied. This analysis opens the opportunity for experimental investigation into the underlying properties of receiver non stationarity through the use of multiple calibration references. In this presentation we will discuss the application of LSR to the analysis of various calibration algorithms, requirements for experimental verification of the theory, and preliminary results from analyzing experiment measurements.

Variability in source sediment contributions by applying different statistic test for a Pyrenean catchment.

PubMed

Palazón, L; Navas, A

2017-06-01

Information on sediment contribution and transport dynamics from the contributing catchments is needed to develop management plans to tackle environmental problems related with effects of fine sediment as reservoir siltation. In this respect, the fingerprinting technique is an indirect technique known to be valuable and effective for sediment source identification in river catchments. Large variability in sediment delivery was found in previous studies in the Barasona catchment (1509 km 2 , Central Spanish Pyrenees). Simulation results with SWAT and fingerprinting approaches identified badlands and agricultural uses as the main contributors to sediment supply in the reservoir. In this study the <63 μm sediment fraction from the surface reservoir sediments (2 cm) are investigated following the fingerprinting procedure to assess how the use of different statistical procedures affects the amounts of source contributions. Three optimum composite fingerprints were selected to discriminate between source contributions based in land uses/land covers from the same dataset by the application of (1) discriminant function analysis; and its combination (as second step) with (2) Kruskal-Wallis H-test and (3) principal components analysis. Source contribution results were different between assessed options with the greatest differences observed for option using #3, including the two step process: principal components analysis and discriminant function analysis. The characteristics of the solutions by the applied mixing model and the conceptual understanding of the catchment showed that the most reliable solution was achieved using #2, the two step process of Kruskal-Wallis H-test and discriminant function analysis. The assessment showed the importance of the statistical procedure used to define the optimum composite fingerprint for sediment fingerprinting applications. Copyright © 2016 Elsevier Ltd. All rights reserved.

Unsupervised Outlier Profile Analysis

PubMed Central

Ghosh, Debashis; Li, Song

2014-01-01

In much of the analysis of high-throughput genomic data, “interesting” genes have been selected based on assessment of differential expression between two groups or generalizations thereof. Most of the literature focuses on changes in mean expression or the entire distribution. In this article, we explore the use of C(α) tests, which have been applied in other genomic data settings. Their use for the outlier expression problem, in particular with continuous data, is problematic but nevertheless motivates new statistics that give an unsupervised analog to previously developed outlier profile analysis approaches. Some simulation studies are used to evaluate the proposal. A bivariate extension is described that can accommodate data from two platforms on matched samples. The proposed methods are applied to data from a prostate cancer study. PMID:25452686

Fast and accurate imputation of summary statistics enhances evidence of functional enrichment

PubMed Central

Pasaniuc, Bogdan; Zaitlen, Noah; Shi, Huwenbo; Bhatia, Gaurav; Gusev, Alexander; Pickrell, Joseph; Hirschhorn, Joel; Strachan, David P.; Patterson, Nick; Price, Alkes L.

2014-01-01

Motivation: Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. Results: In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1–5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case–control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of χ2 association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses. Availability and implementation: Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/. Contact: bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.edu Supplementary information: Supplementary materials are available at Bioinformatics online. PMID:24990607

Fully Bayesian inference for structural MRI: application to segmentation and statistical analysis of T2-hypointensities.

PubMed

Schmidt, Paul; Schmid, Volker J; Gaser, Christian; Buck, Dorothea; Bührlen, Susanne; Förschler, Annette; Mühlau, Mark

2013-01-01

Aiming at iron-related T2-hypointensity, which is related to normal aging and neurodegenerative processes, we here present two practicable approaches, based on Bayesian inference, for preprocessing and statistical analysis of a complex set of structural MRI data. In particular, Markov Chain Monte Carlo methods were used to simulate posterior distributions. First, we rendered a segmentation algorithm that uses outlier detection based on model checking techniques within a Bayesian mixture model. Second, we rendered an analytical tool comprising a Bayesian regression model with smoothness priors (in the form of Gaussian Markov random fields) mitigating the necessity to smooth data prior to statistical analysis. For validation, we used simulated data and MRI data of 27 healthy controls (age: [Formula: see text]; range, [Formula: see text]). We first observed robust segmentation of both simulated T2-hypointensities and gray-matter regions known to be T2-hypointense. Second, simulated data and images of segmented T2-hypointensity were analyzed. We found not only robust identification of simulated effects but also a biologically plausible age-related increase of T2-hypointensity primarily within the dentate nucleus but also within the globus pallidus, substantia nigra, and red nucleus. Our results indicate that fully Bayesian inference can successfully be applied for preprocessing and statistical analysis of structural MRI data.

Prediction of the Electromagnetic Field Distribution in a Typical Aircraft Using the Statistical Energy Analysis

NASA Astrophysics Data System (ADS)

Kovalevsky, Louis; Langley, Robin S.; Caro, Stephane

2016-05-01

Due to the high cost of experimental EMI measurements significant attention has been focused on numerical simulation. Classical methods such as Method of Moment or Finite Difference Time Domain are not well suited for this type of problem, as they require a fine discretisation of space and failed to take into account uncertainties. In this paper, the authors show that the Statistical Energy Analysis is well suited for this type of application. The SEA is a statistical approach employed to solve high frequency problems of electromagnetically reverberant cavities at a reduced computational cost. The key aspects of this approach are (i) to consider an ensemble of system that share the same gross parameter, and (ii) to avoid solving Maxwell's equations inside the cavity, using the power balance principle. The output is an estimate of the field magnitude distribution in each cavity. The method is applied on a typical aircraft structure.

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.

Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series

NASA Technical Reports Server (NTRS)

Vautard, R.; Ghil, M.

1989-01-01

Two dimensions of a dynamical system given by experimental time series are distinguished. Statistical dimension gives a theoretical upper bound for the minimal number of degrees of freedom required to describe the attractor up to the accuracy of the data, taking into account sampling and noise problems. The dynamical dimension is the intrinsic dimension of the attractor and does not depend on the quality of the data. Singular Spectrum Analysis (SSA) provides estimates of the statistical dimension. SSA also describes the main physical phenomena reflected by the data. It gives adaptive spectral filters associated with the dominant oscillations of the system and clarifies the noise characteristics of the data. SSA is applied to four paleoclimatic records. The principal climatic oscillations and the regime changes in their amplitude are detected. About 10 degrees of freedom are statistically significant in the data. Large noise and insufficient sample length do not allow reliable estimates of the dynamical dimension.

A statistically derived index for classifying East Coast fever reactions in cattle challenged with Theileria parva under experimental conditions.

PubMed

Rowlands, G J; Musoke, A J; Morzaria, S P; Nagda, S M; Ballingall, K T; McKeever, D J

2000-04-01

A statistically derived disease reaction index based on parasitological, clinical and haematological measurements observed in 309 5 to 8-month-old Boran cattle following laboratory challenge with Theileria parva is described. Principal component analysis was applied to 13 measures including first appearance of schizonts, first appearance of piroplasms and first occurrence of pyrexia, together with the duration and severity of these symptoms, and white blood cell count. The first principal component, which was based on approximately equal contributions of the 13 variables, provided the definition for the disease reaction index, defined on a scale of 0-10. As well as providing a more objective measure of the severity of the reaction, the continuous nature of the index score enables more powerful statistical analysis of the data compared with that which has been previously possible through clinically derived categories of non-, mild, moderate and severe reactions.

Performance of Between-Study Heterogeneity Measures in the Cochrane Library.

PubMed

Ma, Xiaoyue; Lin, Lifeng; Qu, Zhiyong; Zhu, Motao; Chu, Haitao

2018-05-29

The growth in comparative effectiveness research and evidence-based medicine has increased attention to systematic reviews and meta-analyses. Meta-analysis synthesizes and contrasts evidence from multiple independent studies to improve statistical efficiency and reduce bias. Assessing heterogeneity is critical for performing a meta-analysis and interpreting results. As a widely used heterogeneity measure, the I statistic quantifies the proportion of total variation across studies that is due to real differences in effect size. The presence of outlying studies can seriously exaggerate the I statistic. Two alternative heterogeneity measures, the Ir and Im, have been recently proposed to reduce the impact of outlying studies. To evaluate these measures' performance empirically, we applied them to 20,599 meta-analyses in the Cochrane Library. We found that the Ir and Im have strong agreement with the I, while they are more robust than the I when outlying studies appear.

Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data

PubMed Central

Takayasu, Hideki; Takayasu, Misako

2017-01-01

We extend the concept of statistical symmetry as the invariance of a probability distribution under transformation to analyze binary sign time series data of price difference from the foreign exchange market. We model segments of the sign time series as Markov sequences and apply a local hypothesis test to evaluate the symmetries of independence and time reversion in different periods of the market. For the test, we derive the probability of a binary Markov process to generate a given set of number of symbol pairs. Using such analysis, we could not only segment the time series according the different behaviors but also characterize the segments in terms of statistical symmetries. As a particular result, we find that the foreign exchange market is essentially time reversible but this symmetry is broken when there is a strong external influence. PMID:28542208

Characterizing microstructural features of biomedical samples by statistical analysis of Mueller matrix images

NASA Astrophysics Data System (ADS)

He, Honghui; Dong, Yang; Zhou, Jialing; Ma, Hui

2017-03-01

As one of the salient features of light, polarization contains abundant structural and optical information of media. Recently, as a comprehensive description of polarization property, the Mueller matrix polarimetry has been applied to various biomedical studies such as cancerous tissues detections. In previous works, it has been found that the structural information encoded in the 2D Mueller matrix images can be presented by other transformed parameters with more explicit relationship to certain microstructural features. In this paper, we present a statistical analyzing method to transform the 2D Mueller matrix images into frequency distribution histograms (FDHs) and their central moments to reveal the dominant structural features of samples quantitatively. The experimental results of porcine heart, intestine, stomach, and liver tissues demonstrate that the transformation parameters and central moments based on the statistical analysis of Mueller matrix elements have simple relationships to the dominant microstructural properties of biomedical samples, including the density and orientation of fibrous structures, the depolarization power, diattenuation and absorption abilities. It is shown in this paper that the statistical analysis of 2D images of Mueller matrix elements may provide quantitative or semi-quantitative criteria for biomedical diagnosis.

A Statistical Method for Synthesizing Mediation Analyses Using the Product of Coefficient Approach Across Multiple Trials

PubMed Central

Huang, Shi; MacKinnon, David P.; Perrino, Tatiana; Gallo, Carlos; Cruden, Gracelyn; Brown, C Hendricks

2016-01-01

Mediation analysis often requires larger sample sizes than main effect analysis to achieve the same statistical power. Combining results across similar trials may be the only practical option for increasing statistical power for mediation analysis in some situations. In this paper, we propose a method to estimate: 1) marginal means for mediation path a, the relation of the independent variable to the mediator; 2) marginal means for path b, the relation of the mediator to the outcome, across multiple trials; and 3) the between-trial level variance-covariance matrix based on a bivariate normal distribution. We present the statistical theory and an R computer program to combine regression coefficients from multiple trials to estimate a combined mediated effect and confidence interval under a random effects model. Values of coefficients a and b, along with their standard errors from each trial are the input for the method. This marginal likelihood based approach with Monte Carlo confidence intervals provides more accurate inference than the standard meta-analytic approach. We discuss computational issues, apply the method to two real-data examples and make recommendations for the use of the method in different settings. PMID:28239330

Nanometer scale composition study of MBE grown BGaN performed by atom probe tomography

NASA Astrophysics Data System (ADS)

Bonef, Bastien; Cramer, Richard; Speck, James S.

2017-06-01

Laser assisted atom probe tomography is used to characterize the alloy distribution in BGaN. The effect of the evaporation conditions applied on the atom probe specimens on the mass spectrum and the quantification of the III site atoms is first evaluated. The evolution of the Ga++/Ga+ charge state ratio is used to monitor the strength of the applied field. Experiments revealed that applying high electric fields on the specimen results in the loss of gallium atoms, leading to the over-estimation of boron concentration. Moreover, spatial analysis of the surface field revealed a significant loss of atoms at the center of the specimen where high fields are applied. A good agreement between X-ray diffraction and atom probe tomography concentration measurements is obtained when low fields are applied on the tip. A random distribution of boron in the BGaN layer grown by molecular beam epitaxy is obtained by performing accurate and site specific statistical distribution analysis.

Living Animals in the Classroom: A Meta-Analysis on Learning Outcome and a Treatment-Control Study Focusing on Knowledge and Motivation

ERIC Educational Resources Information Center

Hummel, Eberhard; Randler, Christoph

2012-01-01

Prior research states that the use of living animals in the classroom leads to a higher knowledge but those previous studies have methodological and statistical problems. We applied a meta-analysis and developed a treatment-control study in a middle school classroom. The treatments (film vs. living animal) differed only by the presence of the…

Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites.

PubMed

Ielpo, Pierina; Leardi, Riccardo; Pappagallo, Giuseppe; Uricchio, Vito Felice

2017-06-01

In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project "Improvement of the Regional Agro-meteorological Monitoring Network (2004-2007)". LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.

Magnetic Field-Vector Measurements in Quiescent Prominences via the Hanle Effect: Analysis of Prominences Observed at Pic-Du-Midi and at Sacramento Peak

NASA Technical Reports Server (NTRS)

Bommier, V.; Leroy, J. L.; Sahal-Brechot, S.

1985-01-01

The Hanle effect method for magnetic field vector diagnostics has now provided results on the magnetic field strength and direction in quiescent prominences, from linear polarization measurements in the He I E sub 3 line, performed at the Pic-du-Midi and at Sacramento Peak. However, there is an inescapable ambiguity in the field vector determination: each polarization measurement provides two field vector solutions symmetrical with respect to the line-of-sight. A statistical analysis capable of solving this ambiguity was applied to the large sample of prominences observed at the Pic-du-Midi (Leroy, et al., 1984); the same method of analysis applied to the prominences observed at Sacramento Peak (Athay, et al., 1983) provides results in agreement on the most probable magnetic structure of prominences; these results are detailed. The statistical results were confirmed on favorable individual cases: for 15 prominences observed at Pic-du-Midi, the two-field vectors are pointing on the same side of the prominence, and the alpha angles are large enough with respect to the measurements and interpretation inaccuracies, so that the field polarity is derived without any ambiguity.

In-situ structural integrity evaluation for high-power pulsed spallation neutron source - Effects of cavitation damage on structural vibration

NASA Astrophysics Data System (ADS)

Wan, Tao; Naoe, Takashi; Futakawa, Masatoshi

2016-01-01

A double-wall structure mercury target will be installed at the high-power pulsed spallation neutron source in the Japan Proton Accelerator Research Complex (J-PARC). Cavitation damage on the inner wall is an important factor governing the lifetime of the target-vessel. To monitor the structural integrity of the target vessel, displacement velocity at a point on the outer surface of the target vessel is measured using a laser Doppler vibrometer (LDV). The measured signals can be used for evaluating the damage inside the target vessel because of cyclic loading and cavitation bubble collapse caused by pulsed-beam induced pressure waves. The wavelet differential analysis (WDA) was applied to reveal the effects of the damage on vibrational cycling. To reduce the effects of noise superimposed on the vibration signals on the WDA results, analysis of variance (ANOVA) and analysis of covariance (ANCOVA), statistical methods were applied. Results from laboratory experiments, numerical simulation results with random noise added, and target vessel field data were analyzed by the WDA and the statistical methods. The analyses demonstrated that the established in-situ diagnostic technique can be used to effectively evaluate the structural response of the target vessel.

Potential errors and misuse of statistics in studies on leakage in endodontics.

PubMed

Lucena, C; Lopez, J M; Pulgar, R; Abalos, C; Valderrama, M J

2013-04-01

To assess the quality of the statistical methodology used in studies of leakage in Endodontics, and to compare the results found using appropriate versus inappropriate inferential statistical methods. The search strategy used the descriptors 'root filling' 'microleakage', 'dye penetration', 'dye leakage', 'polymicrobial leakage' and 'fluid filtration' for the time interval 2001-2010 in journals within the categories 'Dentistry, Oral Surgery and Medicine' and 'Materials Science, Biomaterials' of the Journal Citation Report. All retrieved articles were reviewed to find potential pitfalls in statistical methodology that may be encountered during study design, data management or data analysis. The database included 209 papers. In all the studies reviewed, the statistical methods used were appropriate for the category attributed to the outcome variable, but in 41% of the cases, the chi-square test or parametric methods were inappropriately selected subsequently. In 2% of the papers, no statistical test was used. In 99% of cases, a statistically 'significant' or 'not significant' effect was reported as a main finding, whilst only 1% also presented an estimation of the magnitude of the effect. When the appropriate statistical methods were applied in the studies with originally inappropriate data analysis, the conclusions changed in 19% of the cases. Statistical deficiencies in leakage studies may affect their results and interpretation and might be one of the reasons for the poor agreement amongst the reported findings. Therefore, more effort should be made to standardize statistical methodology. © 2012 International Endodontic Journal.

Real-time movement detection and analysis for video surveillance applications

NASA Astrophysics Data System (ADS)

Hueber, Nicolas; Hennequin, Christophe; Raymond, Pierre; Moeglin, Jean-Pierre

2014-06-01

Pedestrian movement along critical infrastructures like pipes, railways or highways, is of major interest in surveillance applications as well as its behavior in urban environment. The goal is to anticipate illicit or dangerous human activities. For this purpose, we propose an all-in-one small autonomous system which delivers high level statistics and reports alerts in specific cases. This situational awareness project leads us to manage efficiently the scene by performing movement analysis. A dynamic background extraction algorithm is developed to reach the degree of robustness against natural and urban environment perturbations and also to match the embedded implementation constraints. When changes are detected in the scene, specific patterns are applied to detect and highlight relevant movements. Depending on the applications, specific descriptors can be extracted and fused in order to reach a high level of interpretation. In this paper, our approach is applied to two operational use cases: pedestrian urban statistics and railway surveillance. In the first case, a grid of prototypes is deployed over a city centre to collect pedestrian movement statistics up to a macroscopic level of analysis. The results demonstrate the relevance of the delivered information; in particular, the flow density map highlights pedestrian preferential paths along the streets. In the second case, one prototype is set next to high speed train tracks to secure the area. The results exhibit a low false alarm rate and assess our approach of a large sensor network for delivering a precise operational picture without overwhelming a supervisor.

Physical and genetic-interaction density reveals functional organization and informs significance cutoffs in genome-wide screens

PubMed Central

Dittmar, John C.; Pierce, Steven; Rothstein, Rodney; Reid, Robert J. D.

2013-01-01

Genome-wide experiments often measure quantitative differences between treated and untreated cells to identify affected strains. For these studies, statistical models are typically used to determine significance cutoffs. We developed a method termed “CLIK” (Cutoff Linked to Interaction Knowledge) that overlays biological knowledge from the interactome on screen results to derive a cutoff. The method takes advantage of the fact that groups of functionally related interacting genes often respond similarly to experimental conditions and, thus, cluster in a ranked list of screen results. We applied CLIK analysis to five screens of the yeast gene disruption library and found that it defined a significance cutoff that differed from traditional statistics. Importantly, verification experiments revealed that the CLIK cutoff correlated with the position in the rank order where the rate of true positives drops off significantly. In addition, the gene sets defined by CLIK analysis often provide further biological perspectives. For example, applying CLIK analysis retrospectively to a screen for cisplatin sensitivity allowed us to identify the importance of the Hrq1 helicase in DNA crosslink repair. Furthermore, we demonstrate the utility of CLIK to determine optimal treatment conditions by analyzing genome-wide screens at multiple rapamycin concentrations. We show that CLIK is an extremely useful tool for evaluating screen quality, determining screen cutoffs, and comparing results between screens. Furthermore, because CLIK uses previously annotated interaction data to determine biologically informed cutoffs, it provides additional insights into screen results, which supplement traditional statistical approaches. PMID:23589890

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis.

PubMed

Quintela-del-Río, Alejandro; Francisco-Fernández, Mario

2011-02-01

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

Statistical model specification and power: recommendations on the use of test-qualified pooling in analysis of experimental data

PubMed Central

Colegrave, Nick

2017-01-01

A common approach to the analysis of experimental data across much of the biological sciences is test-qualified pooling. Here non-significant terms are dropped from a statistical model, effectively pooling the variation associated with each removed term with the error term used to test hypotheses (or estimate effect sizes). This pooling is only carried out if statistical testing on the basis of applying that data to a previous more complicated model provides motivation for this model simplification; hence the pooling is test-qualified. In pooling, the researcher increases the degrees of freedom of the error term with the aim of increasing statistical power to test their hypotheses of interest. Despite this approach being widely adopted and explicitly recommended by some of the most widely cited statistical textbooks aimed at biologists, here we argue that (except in highly specialized circumstances that we can identify) the hoped-for improvement in statistical power will be small or non-existent, and there is likely to be much reduced reliability of the statistical procedures through deviation of type I error rates from nominal levels. We thus call for greatly reduced use of test-qualified pooling across experimental biology, more careful justification of any use that continues, and a different philosophy for initial selection of statistical models in the light of this change in procedure. PMID:28330912

Applying quantitative bias analysis to estimate the plausible effects of selection bias in a cluster randomised controlled trial: secondary analysis of the Primary care Osteoarthritis Screening Trial (POST).

PubMed

Barnett, L A; Lewis, M; Mallen, C D; Peat, G

2017-12-04

Selection bias is a concern when designing cluster randomised controlled trials (c-RCT). Despite addressing potential issues at the design stage, bias cannot always be eradicated from a trial design. The application of bias analysis presents an important step forward in evaluating whether trial findings are credible. The aim of this paper is to give an example of the technique to quantify potential selection bias in c-RCTs. This analysis uses data from the Primary care Osteoarthritis Screening Trial (POST). The primary aim of this trial was to test whether screening for anxiety and depression, and providing appropriate care for patients consulting their GP with osteoarthritis would improve clinical outcomes. Quantitative bias analysis is a seldom-used technique that can quantify types of bias present in studies. Due to lack of information on the selection probability, probabilistic bias analysis with a range of triangular distributions was also used, applied at all three follow-up time points; 3, 6, and 12 months post consultation. A simple bias analysis was also applied to the study. Worse pain outcomes were observed among intervention participants than control participants (crude odds ratio at 3, 6, and 12 months: 1.30 (95% CI 1.01, 1.67), 1.39 (1.07, 1.80), and 1.17 (95% CI 0.90, 1.53), respectively). Probabilistic bias analysis suggested that the observed effect became statistically non-significant if the selection probability ratio was between 1.2 and 1.4. Selection probability ratios of > 1.8 were needed to mask a statistically significant benefit of the intervention. The use of probabilistic bias analysis in this c-RCT suggested that worse outcomes observed in the intervention arm could plausibly be attributed to selection bias. A very large degree of selection of bias was needed to mask a beneficial effect of intervention making this interpretation less plausible.

mvp - an open-source preprocessor for cleaning duplicate records and missing values in mass spectrometry data.

PubMed

Lee, Geunho; Lee, Hyun Beom; Jung, Byung Hwa; Nam, Hojung

2017-07-01

Mass spectrometry (MS) data are used to analyze biological phenomena based on chemical species. However, these data often contain unexpected duplicate records and missing values due to technical or biological factors. These 'dirty data' problems increase the difficulty of performing MS analyses because they lead to performance degradation when statistical or machine-learning tests are applied to the data. Thus, we have developed missing values preprocessor (mvp), an open-source software for preprocessing data that might include duplicate records and missing values. mvp uses the property of MS data in which identical chemical species present the same or similar values for key identifiers, such as the mass-to-charge ratio and intensity signal, and forms cliques via graph theory to process dirty data. We evaluated the validity of the mvp process via quantitative and qualitative analyses and compared the results from a statistical test that analyzed the original and mvp-applied data. This analysis showed that using mvp reduces problems associated with duplicate records and missing values. We also examined the effects of using unprocessed data in statistical tests and examined the improved statistical test results obtained with data preprocessed using mvp.

Combine bivariate statistics analysis and multivariate statistics analysis to assess landslide susceptibility in Chen-Yu-Lan watershed, Nantou, Taiwan.

NASA Astrophysics Data System (ADS)

Ngan Nguyen, Thi To; Liu, Cheng-Chien

2013-04-01

How landslides occurred and which factors triggered and sped up landslide occurrences were usually asked by researchers in the past decades. Many investigations carried out in many places in the world to finding out methods that predict and prevent damages from landslides phenomena. Chen-Yu-Lan River watershed is reputed as a 'hot pot' of landslide researches in Taiwan by its complicated geological structures with the significant tectonic fault systems and steeply mountainous terrain. Beside annual high precipitation concentration and the abrupt slopes, some natural disaster, as typhoons (Sinlaku-2008, Kalmaegi-2008, and Marakot-2009) and earthquake (Chi-Chi earthquake-1999) are also the triggered factors cause landslides with serious damages in this place. This research expresses the quantitative approaches to generate landslide susceptible map for Chen-Yu-Lan watershed, a mountainous area in the central Taiwan. Landslide inventories data, which were detected from the Formosat-2 imageries for eight years from 2004 to 2011, were applied to carry out landslide susceptibility mapping. Bivariate statistics analysis and multivariate statistics analysis would be applied to calculate susceptible index of landslides. The weights of parameters were computed based on landslide data for eight years from 2004 to 2011. To validate effective levels of factors to landslide occurrences, this method built some multivariate algorithms and compared these results with real landslide occurrences. Besides this method, the historical data of landslides were also used to assess and classify landslide susceptibility levels. From long-term landslide data, relation between landslide susceptibility levels and landslide repetition was assigned. The results demonstrated differently effective levels of potential factors, such as, slope gradient, drainage density, lithology and land use to landslide phenomena. The results also showed logical relationship between weights and characteristics of factors' classes. Depending on these results be able to help planning managers localize the high risk areas of landslide or safely areas by building and human activities.

Analysis tools for discovering strong parity violation at hadron colliders

NASA Astrophysics Data System (ADS)

Backović, Mihailo; Ralston, John P.

2011-07-01

Several arguments suggest parity violation may be observable in high energy strong interactions. We introduce new analysis tools to describe the azimuthal dependence of multiparticle distributions, or “azimuthal flow.” Analysis uses the representations of the orthogonal group O(2) and dihedral groups DN necessary to define parity completely in two dimensions. Classification finds that collective angles used in event-by-event statistics represent inequivalent tensor observables that cannot generally be represented by a single “reaction plane.” Many new parity-violating observables exist that have never been measured, while many parity-conserving observables formerly lumped together are now distinguished. We use the concept of “event-shape sorting” to suggest separating right- and left-handed events, and we discuss the effects of transverse and longitudinal spin. The analysis tools are statistically robust, and can be applied equally to low or high multiplicity events at the Tevatron, RHIC or RHIC Spin, and the LHC.

Progressive Failure And Life Prediction of Ceramic and Textile Composites

NASA Technical Reports Server (NTRS)

Xue, David Y.; Shi, Yucheng; Katikala, Madhu; Johnston, William M., Jr.; Card, Michael F.

1998-01-01

An engineering approach to predict the fatigue life and progressive failure of multilayered composite and textile laminates is presented. Analytical models which account for matrix cracking, statistical fiber failures and nonlinear stress-strain behavior have been developed for both composites and textiles. The analysis method is based on a combined micromechanics, fracture mechanics and failure statistics analysis. Experimentally derived empirical coefficients are used to account for the interface of fiber and matrix, fiber strength, and fiber-matrix stiffness reductions. Similar approaches were applied to textiles using Repeating Unit Cells. In composite fatigue analysis, Walker's equation is applied for matrix fatigue cracking and Heywood's formulation is used for fiber strength fatigue degradation. The analysis has been compared with experiment with good agreement. Comparisons were made with Graphite-Epoxy, C/SiC and Nicalon/CAS composite materials. For textile materials, comparisons were made with triaxial braided and plain weave materials under biaxial or uniaxial tension. Fatigue predictions were compared with test data obtained from plain weave C/SiC materials tested at AS&M. Computer codes were developed to perform the analysis. Composite Progressive Failure Analysis for Laminates is contained in the code CPFail. Micromechanics Analysis for Textile Composites is contained in the code MicroTex. Both codes were adapted to run as subroutines for the finite element code ABAQUS and CPFail-ABAQUS and MicroTex-ABAQUS. Graphic user interface (GUI) was developed to connect CPFail and MicroTex with ABAQUS.

Fulfilling the law of a single independent variable and improving the result of mathematical educational research

NASA Astrophysics Data System (ADS)

Pardimin, H.; Arcana, N.

2018-01-01

Many types of research in the field of mathematics education apply the Quasi-Experimental method and statistical analysis use t-test. Quasi-experiment has a weakness that is difficult to fulfil “the law of a single independent variable”. T-test also has a weakness that is a generalization of the conclusions obtained is less powerful. This research aimed to find ways to reduce the weaknesses of the Quasi-experimental method and improved the generalization of the research results. The method applied in the research was a non-interactive qualitative method, and the type was concept analysis. Concepts analysed are the concept of statistics, research methods of education, and research reports. The result represented a way to overcome the weaknesses of quasi-Experiments and T-test. In addition, the way was to apply a combination of Factorial Design and Balanced Design, which the authors refer to as Factorial-Balanced Design. The advantages of this design are: (1) almost fulfilling “the low of single independent variable” so no need to test the similarity of the academic ability, (2) the sample size of the experimental group and the control group became larger and equal; so it becomes robust to deal with violations of the assumptions of the ANOVA test.

A brief introduction to probability.

PubMed

Di Paola, Gioacchino; Bertani, Alessandro; De Monte, Lavinia; Tuzzolino, Fabio

2018-02-01

The theory of probability has been debated for centuries: back in 1600, French mathematics used the rules of probability to place and win bets. Subsequently, the knowledge of probability has significantly evolved and is now an essential tool for statistics. In this paper, the basic theoretical principles of probability will be reviewed, with the aim of facilitating the comprehension of statistical inference. After a brief general introduction on probability, we will review the concept of the "probability distribution" that is a function providing the probabilities of occurrence of different possible outcomes of a categorical or continuous variable. Specific attention will be focused on normal distribution that is the most relevant distribution applied to statistical analysis.

Spatio-temporal dependencies between hospital beds, physicians and health expenditure using visual variables and data classification in statistical table

NASA Astrophysics Data System (ADS)

Medyńska-Gulij, Beata; Cybulski, Paweł

2016-06-01

This paper analyses the use of table visual variables of statistical data of hospital beds as an important tool for revealing spatio-temporal dependencies. It is argued that some of conclusions from the data about public health and public expenditure on health have a spatio-temporal reference. Different from previous studies, this article adopts combination of cartographic pragmatics and spatial visualization with previous conclusions made in public health literature. While the significant conclusions about health care and economic factors has been highlighted in research papers, this article is the first to apply visual analysis to statistical table together with maps which is called previsualisation.

Big Data and Neuroimaging.

PubMed

Webb-Vargas, Yenny; Chen, Shaojie; Fisher, Aaron; Mejia, Amanda; Xu, Yuting; Crainiceanu, Ciprian; Caffo, Brian; Lindquist, Martin A

2017-12-01

Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.

Deciphering the Landauer-Büttiker Transmission Function from Single Molecule Break Junction Experiments

NASA Astrophysics Data System (ADS)

Reuter, Matthew; Tschudi, Stephen

When investigating the electrical response properties of molecules, experiments often measure conductance whereas computation predicts transmission probabilities. Although the Landauer-Büttiker theory relates the two in the limit of coherent scattering through the molecule, a direct comparison between experiment and computation can still be difficult. Experimental data (specifically that from break junctions) is statistical and computational results are deterministic. Many studies compare the most probable experimental conductance with computation, but such an analysis discards almost all of the experimental statistics. In this work we develop tools to decipher the Landauer-Büttiker transmission function directly from experimental statistics and then apply them to enable a fairer comparison between experimental and computational results.

The statistical kinematical theory of X-ray diffraction as applied to reciprocal-space mapping

PubMed

Nesterets; Punegov

2000-11-01

The statistical kinematical X-ray diffraction theory is developed to describe reciprocal-space maps (RSMs) from deformed crystals with defects of the structure. The general solutions for coherent and diffuse components of the scattered intensity in reciprocal space are derived. As an example, the explicit expressions for intensity distributions in the case of spherical defects and of a mosaic crystal were obtained. The theory takes into account the instrumental function of the triple-crystal diffractometer and can therefore be used for experimental data analysis.

Hydrological responses to dynamically and statistically downscaled climate model output

USGS Publications Warehouse

Wilby, R.L.; Hay, L.E.; Gutowski, W.J.; Arritt, R.W.; Takle, E.S.; Pan, Z.; Leavesley, G.H.; Clark, M.P.

2000-01-01

Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically downscaled output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis. A distributed hydrological model was then applied to the downscaled data. Relative to raw NCEP output, downscaled climate variables provided more realistic stimulations of basin scale hydrology. However, the results highlight the sensitivity of modeled processes to the choice of downscaling technique, and point to the need for caution when interpreting future hydrological scenarios.

[PROGNOSTIC MODELS IN MODERN MANAGEMENT OF VULVAR CANCER].

PubMed

Tsvetkov, Ch; Gorchev, G; Tomov, S; Nikolova, M; Genchev, G

2016-01-01

The aim of the research was to evaluate and analyse prognosis and prognostic factors in patients with squamous cell vulvar carcinoma after primary surgery with individual approach applied during the course of treatment. In the period between January 2000 and July 2010, 113 patients with squamous cell carcinoma of the vulva were diagnosed and operated on at Gynecologic Oncology Clinic of Medical University, Pleven. All the patients were monitored at the same clinic. Individual approach was applied to each patient and whenever it was possible, more conservative operative techniques were applied. The probable clinicopathological characteristics influencing the overall survival and recurrence free survival were analyzed. Univariate statistical analysis and Cox regression analysis were made in order to evaluate the characteristics, which were statistically significant for overall survival and survival without recurrence. A multivariate logistic regression analysis (Forward Wald procedure) was applied to evaluate the combined influence of the significant factors. While performing the multivariate analysis, the synergic effect of the independent prognostic factors of both kinds of survivals was also evaluated. Approaching individually each patient, we applied the following operative techniques: 1. Deep total radical vulvectomy with separate incisions for lymph dissection (LD) or without dissection--68 (60.18 %) patients. 2. En-bloc vulvectomy with bilateral LD without vulva reconstruction--10 (8.85%) 3. Modified radical vulvactomy (hemivulvectomy, patial vulvactomy)--25 (22.02%). 4. wide-local excision--3 (2.65%). 5. Simple (total /partial) vulvectomy--5 (4.43%) patients. 6. En-bloc resection with reconstruction--2 (1.77%) After a thorough analysis of the overall survival and recurrence free survival, we made the conclusion that the relapse occurrence and clinical stage of FIGO were independent prognostic factors for overall survival and the independent prognostic factors for recurrence free survival were: metastatic inguinal nodes (unilateral or bilateral), tumor size (above or below 3 cm) and lymphovascular space invasion. On the basis of these results we created two prognostic models: 1. A prognostic model of overall survival 2. A prognostic model for survival without recurrence. Following the surgical staging of the disease, were able to gather and analyse important clinicopathological indexes, which gave us the opportunity to form prognostic groups for overall survival and recurrence-free survival.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline

PubMed Central

2013-01-01

Background As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. Results We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS A : DE genes with non-zero effect sizes in all studies, (2) HS B : DE genes with non-zero effect sizes in one or more studies and (3) HS r : DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. Conclusions The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS A , HS B , and HS r ). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author’s publication website. PMID:24359104

Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline.

PubMed

Chang, Lun-Ching; Lin, Hui-Min; Sibille, Etienne; Tseng, George C

2013-12-21

As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS(A): DE genes with non-zero effect sizes in all studies, (2) HS(B): DE genes with non-zero effect sizes in one or more studies and (3) HS(r): DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS(A), HS(B), and HS(r)). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author's publication website.

Analysis of decay chains of superheavy nuclei produced in the 249Bk+48Ca and 243Am+48Ca reactions

NASA Astrophysics Data System (ADS)

Zlokazov, V. B.; Utyonkov, V. K.

2017-07-01

The analysis of decay chains starting at superheavy nuclei 293Ts and 289Mc is presented. The spectroscopic properties of nuclei identified during the experiments using the 249Bk+48Ca and 243Am+48Ca reactions studied at the gas-filled separators DGFRS, TASCA and BGS are considered. We present the analysis of decay data using widely adopted statistical methods and applying them to the short decay chains of parent odd-Z nuclei. We find out that the recently suggested method of analyzing decay chains by Forsberg et al may lead to questionable conclusions when applied for the analysis of radioactive decays. Our discussion demonstrates reasonable congruence of α-particle energies and decay times of nuclei assigned to isotopes 289Mc, 285Nh and 281Rg observed in both reactions.

Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data

NASA Astrophysics Data System (ADS)

Kacprzak, T.; Kirk, D.; Friedrich, O.; Amara, A.; Refregier, A.; Marian, L.; Dietrich, J. P.; Suchyta, E.; Aleksić, J.; Bacon, D.; Becker, M. R.; Bonnett, C.; Bridle, S. L.; Chang, C.; Eifler, T. F.; Hartley, W. G.; Huff, E. M.; Krause, E.; MacCrann, N.; Melchior, P.; Nicola, A.; Samuroff, S.; Sheldon, E.; Troxel, M. A.; Weller, J.; Zuntz, J.; Abbott, T. M. C.; Abdalla, F. B.; Armstrong, R.; Benoit-Lévy, A.; Bernstein, G. M.; Bernstein, R. A.; Bertin, E.; Brooks, D.; Burke, D. L.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Castander, F. J.; Crocce, M.; D'Andrea, C. B.; da Costa, L. N.; Desai, S.; Diehl, H. T.; Evrard, A. E.; Neto, A. Fausti; Flaugher, B.; Fosalba, P.; Frieman, J.; Gerdes, D. W.; Goldstein, D. A.; Gruen, D.; Gruendl, R. A.; Gutierrez, G.; Honscheid, K.; Jain, B.; James, D. J.; Jarvis, M.; Kuehn, K.; Kuropatkin, N.; Lahav, O.; Lima, M.; March, M.; Marshall, J. L.; Martini, P.; Miller, C. J.; Miquel, R.; Mohr, J. J.; Nichol, R. C.; Nord, B.; Plazas, A. A.; Romer, A. K.; Roodman, A.; Rykoff, E. S.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Swanson, M. E. C.; Tarle, G.; Thomas, D.; Vikram, V.; Walker, A. R.; Zhang, Y.; DES Collaboration

2016-12-01

Shear peak statistics has gained a lot of attention recently as a practical alternative to the two-point statistics for constraining cosmological parameters. We perform a shear peak statistics analysis of the Dark Energy Survey (DES) Science Verification (SV) data, using weak gravitational lensing measurements from a 139 deg2 field. We measure the abundance of peaks identified in aperture mass maps, as a function of their signal-to-noise ratio, in the signal-to-noise range 04 would require significant corrections, which is why we do not include them in our analysis. We compare our results to the cosmological constraints from the two-point analysis on the SV field and find them to be in good agreement in both the central value and its uncertainty. We discuss prospects for future peak statistics analysis with upcoming DES data.