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Statistical Simulation

Author: Todd C. Headrick
Publisher: CRC Press
ISBN: 9781420064919
Size: 74.90 MB
Format: PDF, ePub, Docs
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Although power method polynomials based on the standard normal distributions have been used in many different contexts for the past 30 years, it was not until recently that the probability density function (pdf) and cumulative distribution function (cdf) were derived and made available. Focusing on both univariate and multivariate nonnormal data generation, Statistical Simulation: Power Method Polynomials and Other Transformations presents techniques for conducting a Monte Carlo simulation study. It shows how to use power method polynomials for simulating univariate and multivariate nonnormal distributions with specified cumulants and correlation matrices. The book first explores the methodology underlying the power method, before demonstrating this method through examples of standard normal, logistic, and uniform power method pdfs. It also discusses methods for improving the performance of a simulation based on power method polynomials. The book then develops simulation procedures for systems of linear statistical models, intraclass correlation coefficients, and correlated continuous variates and ranks. Numerical examples and results from Monte Carlo simulations illustrate these procedures. The final chapter describes how the g-and-h and generalized lambda distribution (GLD) transformations are special applications of the more general multivariate nonnormal data generation approach. Throughout the text, the author employs Mathematica® in a range of procedures and offers the source code for download online. Written by a longtime researcher of the power method, this book explains how to simulate nonnormal distributions via easy-to-use power method polynomials. By using the methodology and techniques developed in the text, readers can evaluate different transformations in terms of comparing percentiles, measures of central tendency, goodness-of-fit tests, and more.

Exam Prep For Statistical Simulation Power Method Polynomials And Other Transformations

Author: Dave Mason
Publisher: Rico Publications
Size: 18.71 MB
Format: PDF, ePub
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5,600 Exam Prep questions and answers. Ebooks, Textbooks, Courses, Books Simplified as questions and answers by Rico Publications. Very effective study tools especially when you only have a limited amount of time. They work with your textbook or without a textbook and can help you to review and learn essential terms, people, places, events, and key concepts.

Monte Carlo Simulation Based Statistical Modeling

Author: Ding-Geng (Din) Chen
Publisher: Springer
ISBN: 9811033072
Size: 14.90 MB
Format: PDF, Kindle
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This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.

Simulating Data With Sas

Author: Rick Wicklin
Publisher: SAS Institute
ISBN: 1612903320
Size: 31.26 MB
Format: PDF, ePub, Mobi
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Data simulation is a fundamental technique in statistical programming and research. Rick Wicklin's Simulating Data with SAS brings together the most useful algorithms and the best programming techniques for efficient data simulation in an accessible how-to book for practicing statisticians and statistical programmers. This book discusses in detail how to simulate data from common univariate and multivariate distributions, and how to use simulation to evaluate statistical techniques. It also covers simulating correlated data, data for regression models, spatial data, and data with given moments. It provides tips and techniques for beginning programmers, and offers libraries of functions for advanced practitioners. As the first book devoted to simulating data across a range of statistical applications, Simulating Data with SAS is an essential tool for programmers, analysts, researchers, and students who use SAS software.SAS Products and Releases: Base SAS: 9.3 SAS/ETS: 9.3 SAS/IML: 9.3 SAS/STAT: 9.3 Operating Systems: All

Handbook Of Psychology Research Methods In Psychology

Author: Irving B. Weiner
Publisher: John Wiley & Sons
ISBN: 1118282035
Size: 54.11 MB
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Psychology is of interest to academics from many fields, as well as to the thousands of academic and clinical psychologists and general public who can't help but be interested in learning more about why humans think and behave as they do. This award-winning twelve-volume reference covers every aspect of the ever-fascinating discipline of psychology and represents the most current knowledge in the field. This ten-year revision now covers discoveries based in neuroscience, clinical psychology's new interest in evidence-based practice and mindfulness, and new findings in social, developmental, and forensic psychology.

Real Data Analysis

Author: Shlomo S. Sawilowsky
Publisher: Information Age Publishing
ISBN: 9781593115654
Size: 75.57 MB
Format: PDF, ePub
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The invited authors of this edited volume have been prolific in the arena of Real Data Analysis (RDA) as it applies to the social and behavioral sciences, especially in the disciplines of education and psychology. Combined, this brain trust represents 3,247 articles in refereed journals, 127 books published, US $45.3 Million in extramural research funding, 34 teaching and 92 research awards, serve(d) as Editor/Assistant Editor/Editorial Board Member for 95 peer reviewed journals, and provide( d) ad hoc reviews for 362 journals. Their enormous footprint on real data analysis is showcased for professors, researchers, educators, administrators, and graduate students in the second text in the AERA/SIG ES Quantitative Methods series.

Simulating Univariate And Multivariate Nonnormal Distributions Based On A System Of Power Method Distributions

Author: Flaviu-Adrian Hodis
Size: 55.16 MB
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The purpose of this dissertation was to develop a system of power method non-normal distributions based on polynomial transformations of order three. A primary focus was to develop the methodology to provide users the ability to simulate univariate and multivariate non-normal distributions with specified standardized cumulants and arbitrary correlation matrices in a computationally efficient manner. The moment-matching methodology used to derive a particular family of power method distributions and its associated boundary condition in terms of skew and kurtosis is general provided the required moments exist. For example, a family of distributions could be based on a transformation of a normal, triangular, uniform, t , or logistic distribution. However, in view of this investigation, the system of distributions that appears most promising in the context of both univariate and multivariate data generation is a system based on standard (a) normal, (b) logistic, and (c) uniform distributions. The system of non-normal distributions studied in this investigation was demonstrated to have valid power method probability density functions (pdfs) and distribution functions. The specific boundary condition for each family of pdfs within this system was also provided. In view of this, it was demonstrated that the computation of probabilities, percentiles, and trimmed means could be done with relative ease. Numerical examples and graphs of power method pdfs are provided to confirm and illustrate the methodology. It was also shown how the power method could be applied in context of distribution fitting using real-world data. Monte Carlo results are provided to demonstrate that the power method accurately generates the specified cumulants and correlations both within and between the system's non-normal distributions.