Download or read book Nonparametric Monte Carlo Tests for Multivariate Distributions written by Li-Xing Zhu and published by . This book was released on 2000 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Monte Carlo Tests and Their Applications written by Li-Xing Zhu and published by Springer Science & Business Media. This book was released on 2006-04-08 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fundamental issue in statistical analysis is testing the fit of a particular probability model to a set of observed data. Monte Carlo approximation to the null distribution of the test provides a convenient and powerful means of testing model fit. Nonparametric Monte Carlo Tests and Their Applications proposes a new Monte Carlo-based methodology to construct this type of approximation when the model is semistructured. When there are no nuisance parameters to be estimated, the nonparametric Monte Carlo test can exactly maintain the significance level, and when nuisance parameters exist, this method can allow the test to asymptotically maintain the level. The author addresses both applied and theoretical aspects of nonparametric Monte Carlo tests. The new methodology has been used for model checking in many fields of statistics, such as multivariate distribution theory, parametric and semiparametric regression models, multivariate regression models, varying-coefficient models with longitudinal data, heteroscedasticity, and homogeneity of covariance matrices. This book will be of interest to both practitioners and researchers investigating goodness-of-fit tests and resampling approximations. Every chapter of the book includes algorithms, simulations, and theoretical deductions. The prerequisites for a full appreciation of the book are a modest knowledge of mathematical statistics and limit theorems in probability/empirical process theory. The less mathematically sophisticated reader will find Chapters 1, 2 and 6 to be a comprehensible introduction on how and where the new method can apply and the rest of the book to be a valuable reference for Monte Carlo test approximation and goodness-of-fit tests. Lixing Zhu is Associate Professor of Statistics at the University of Hong Kong. He is a winner of the Humboldt Research Award at Alexander-von Humboldt Foundation of Germany and an elected Fellow of the Institute of Mathematical Statistics. From the reviews: "These lecture notes discuss several topics in goodness-of-fit testing, a classical area in statistical analysis. ... The mathematical part contains detailed proofs of the theoretical results. Simulation studies illustrate the quality of the Monte Carlo approximation. ... this book constitutes a recommendable contribution to an active area of current research." Winfried Stute for Mathematical Reviews, Issue 2006 "...Overall, this is an interesting book, which gives a nice introduction to this new and specific field of resampling methods." Dongsheng Tu for Biometrics, September 2006
Download or read book A Monte Carlo Investigation of Nonparametric One way Multivariate Analysis of Variance in the Two group Case written by Rebecca Zwick and published by . This book was released on 1983 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Monte Carlo Simulation written by Christopher Z. Mooney and published by SAGE. This book was released on 1997-04-07 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aimed at researchers across the social sciences, this book explains the logic behind the Monte Carlo simulation method and demonstrates its uses for social and behavioural research.
Download or read book Random Number Generation and Monte Carlo Methods written by James E. Gentle and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monte Carlo simulation has become one of the most important tools in all fields of science. This book surveys the basic techniques and principles of the subject, as well as general techniques useful in more complicated models and in novel settings. The emphasis throughout is on practical methods that work well in current computing environments.
Download or read book Nonparametric Methods in Multivariate Analysis written by Madan Lal Puri and published by . This book was released on 1971 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Approximate Small sample Distributions for Multivariate Two sample Nonparametric Tests written by Ralph A. Bradley and published by . This book was released on 1970 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: The multivariate, two-sample, nonparametric, location problem is considered. Emphasis is on approximate significance tests, important in applications since generally useful tables are precluded because of inherent correlations between variates. Multivariate randomization, ranksum and normal scores tests are considered. The first four moments for each test statistic, conditional on observed samples, are obtained under an hypothesis implying interchangeability of observation vectors between the two samples. Approximate sampling distributions for the test statistics are fitted yielding the approximate test procedures. While there is no way to evaluate the goodness of these approximations generally, a specific example is studied where Monte Carlo results are used to check the approximate methods. (Author).
Download or read book A Monte Carlo Investigation of Multiple Comparison Procedures for Nonparametric Pairwise Comparisons of Location written by Michael Allen Seaman and published by . This book was released on 1990 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Tests for Common But Unspecified Population Distributions written by Gordon Anderson and published by . This book was released on 1994 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Using Monte Carlo Normal Distributions to Evaluate Structural Models with Nonnormal Data written by Siavash Jalal and published by . This book was released on 2017 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the main problems of statistical inference in Structural Equation Modeling (SEM) is the overall goodness of fit test. Many statistical theories have been developed based on asymptotic distributions of test statistics. When the model includes a large number of variables or the population is not from the multivariate normal distribution, the rates of convergence of these asymptotic distributions are very slow, and thus in these situations the asymptotic distributions do not approximate the distribution of the test statistics very well. Modifications to theoretical models and also bootstrap methods have been developed by researchers to improve the accuracy of hypothesis testing, mainly accuracy of Type I error, but when the sample size is small or the number of variables is large those methods have their limitations. Here we propose a Monte Carlo test that is able to control Type I error with more accuracy and it overcomes some of the limitations in the bootstrapping and theoretical approaches. Our simulation study shows that the suggested Monte Carlo test has more accurate observed significance level, as compared to other tests. Problems that occur in the bootstrapping are highlighted and it is shown that the new Monte Carlo test can overcome those problems. A power analysis shows that the new test has a reasonable power.
Download or read book Monte Carlo Simulation Based Statistical Modeling written by Ding-Geng (Din) Chen and published by Springer. This book was released on 2017-02-01 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Nonparametric Tests for Common But Unspecified Population Distributions a Monte Carlo Comparison written by Anderson, Gordon and published by . This book was released on 1994 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Markov Chain Monte Carlo Procedures for Sampling from Multivariate Distributions written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Generating samples from multivariate distributions efficiently is an important task in Monte Carlo integration and many other stochastic simulation problems. Markov chain Monte Carlo has been shown to be very efficient compared to "conventional methods", especially when many dimensions are involved. In this article we propose a Hit-and-Run sampler in combination with the Ratio-of-Uniforms method. We show that it is well suited for an algorithm to generate points from quite arbitrary distributions, which include all log-concave distributions. The algorithm works automatically in the sense that only the mode (or an approximation of it) and an oracle is required, i.e., a subroutine that returns the value of the density function at any point x. We show that the number of evaluations of the density increases slowly with dimension. An implementation of these algorithms in C is available from the authors. (author's abstract).
Download or read book Method of Statistical Testing written by I︠U︡liĭ Anatolʹevich Shreĭder and published by . This book was released on 1964 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Smooth Nonparametric Multivariate Mixed Data Location Scale Test written by Jeffrey Racine and published by . This book was released on 2017 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: A number of tests have been proposed for assessing the location-scale assumption that is often invoked by practitioners. Existing approaches include Kolmogorov-Smirnov and Cramer-von-Mises statistics that each involve measures of divergence between unknown joint distribution functions and products of marginal distributions. In practice, the unknown distribution functions embedded in these statistics are approximated using non-smooth empirical distribution functions. We demonstrate how replacing the non-smooth distributions with their kernel-smoothed counter-parts can lead to substantial power improvements. In so doing we extend existing approaches to the smooth multivariate and mixed continuous and discrete data setting thereby extending the reach of existing approaches. Theoretical underpinnings are provided, Monte Carlo simulations are undertaken to assess finite-sample performance, and illustrative applications are provided.
Download or read book Monte Carlo Investigations of Hypothesis Testing Procedure Involving Multivariate and Variance ratio Statistics in a Two way Layout with One Random Effect written by Rodney Lee Rosse and published by . This book was released on 1972 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introducing Monte Carlo Methods with R written by Christian Robert and published by Springer Science & Business Media. This book was released on 2010 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the main tools used in statistical simulation from a programmer’s point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison.
