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An Em Ave Algorithm For Variance Component Estimation With Simple Diagnostics
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Book Synopsis An EM-AVE Algorithm for Variance Component Estimation with Simple Diagnostics by : Ronald R. Hocking
Download or read book An EM-AVE Algorithm for Variance Component Estimation with Simple Diagnostics written by Ronald R. Hocking and published by . This book was released on 1992 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper is to describe a new approach to computing estimates of variance components in a general class of unbalanced, mixed linear models. The computation is based on the method of restricted maximum likelihood (REML) and uses a combination of the EM algorithm and the AVE approach which has been applied in balanced designs. An added feature of the procedure is that it yields simple diagnostic information on the data and the model assumptions. A numerical example which illustrates the procedure and the diagnostic analysis is included.
Book Synopsis Estimation of Variance Components and Diagnostic Analysis in Unbalanced Mixed Linear Models by : Marco Vinicio Gómez Meza
Download or read book Estimation of Variance Components and Diagnostic Analysis in Unbalanced Mixed Linear Models written by Marco Vinicio Gómez Meza and published by . This book was released on 1993 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Variance Component Estimation with Model-based Diagnostics by : Ronald R. Hocking
Download or read book Variance Component Estimation with Model-based Diagnostics written by Ronald R. Hocking and published by . This book was released on 1988 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of estimating variance components in mixed linear models has no satisfactory solution for the unbalanced data case. Further, there has been little work on diagnostic methods for assessing the data and the model. In this article, we propose a new class of unbiased estimators. These estimators have simple closed form expressions allowing for small sample analysis and easy computations. The structure of the estimators reveals natural diagnostics for examining the data and the model assumptions. The source of negative estimates of variance components is revealed. Limited efficiency studies indicate that these estimators are comparable to existing estimators. The estimators are illustrated by two numerical examples that reveal the importance of the diagnostic analysis.
Book Synopsis Estimation of Variance Components by : Tito Roque Vasquez Rojas
Download or read book Estimation of Variance Components written by Tito Roque Vasquez Rojas and published by . This book was released on 1994 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Analysis of Variance in Behavioral Science by : Lynne Edwards
Download or read book Applied Analysis of Variance in Behavioral Science written by Lynne Edwards and published by CRC Press. This book was released on 1993-06-16 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: A reference devoted to the discussion of analysis of variance (ANOVA) techniques. It presents ANOVA as a research design, a collection of statistical models, an analysis model, and an arithmetic summary of data. Discussion focuses primarily on univariate data, but multivariate generalizations are to
Book Synopsis Methods and Applications of Linear Models by : Ronald R. Hocking
Download or read book Methods and Applications of Linear Models written by Ronald R. Hocking and published by John Wiley & Sons. This book was released on 2013-07-01 with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the Second Edition "An essential desktop reference book . . . it should definitely be on your bookshelf." —Technometrics A thoroughly updated book, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition features innovative approaches to understanding and working with models and theory of linear regression. The Third Edition provides readers with the necessary theoretical concepts, which are presented using intuitive ideas rather than complicated proofs, to describe the inference that is appropriate for the methods being discussed. The book presents a unique discussion that combines coverage of mathematical theory of linear models with analysis of variance models, providing readers with a comprehensive understanding of both the theoretical and technical aspects of linear models. With a new focus on fixed effects models, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition also features: Newly added topics including least squares, the cell means model, and graphical inspection of data in the AVE method Frequent conceptual and numerical examples for clarifying the statistical analyses and demonstrating potential pitfalls Graphics and computations developed using JMP® software to accompany the concepts and techniques presented Numerous exercises presented to test readers and deepen their understanding of the material An ideal book for courses on linear models and linear regression at the undergraduate and graduate levels, the Third Edition of Methods and Applications of Linear Models: Regression and the Analysis of Variance is also a valuable reference for applied statisticians and researchers who utilize linear model methodology.
Book Synopsis A Simplified Algorithm for the W-transformation in Variance Component Estimation by : J. H. Goodnight
Download or read book A Simplified Algorithm for the W-transformation in Variance Component Estimation written by J. H. Goodnight and published by . This book was released on 1978 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A New Approach to Variance Component Estimation with Diagnostic Implications by : Ronald R. Hocking
Download or read book A New Approach to Variance Component Estimation with Diagnostic Implications written by Ronald R. Hocking and published by . This book was released on 1991 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: Closed form expressions are developed for the estimators of functions of the variance components in balanced, mixed, linear models. These estimators are averages of sample covariances (variances) which offer diagnostic information on the data and the model. The cause of negative estimates may be revealed. Examples illustrate the basic concepts.
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1995 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Linear Statistical Models by : Michael H. Kutner
Download or read book Applied Linear Statistical Models written by Michael H. Kutner and published by McGraw-Hill/Irwin. This book was released on 2005 with total page 1396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.
Book Synopsis Introduction to Small Area Estimation Techniques by : Asian Development Bank
Download or read book Introduction to Small Area Estimation Techniques written by Asian Development Bank and published by Asian Development Bank. This book was released on 2020-05-01 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways. It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals.
Book Synopsis Generalized Linear Mixed Models by : Charles E. McCulloch
Download or read book Generalized Linear Mixed Models written by Charles E. McCulloch and published by IMS. This book was released on 2003 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wiley Series in Probability and Statistics A modern perspective on mixed models The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features: * A review of the basics of linear models and linear mixed models * Descriptions of models for nonnormal data, including generalized linear and nonlinear models * Analysis and illustration of techniques for a variety of real data sets * Information on the accommodation of longitudinal data using these models * Coverage of the prediction of realized values of random effects * A discussion of the impact of computing issues on mixed models
Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Book Synopsis Theory of Multivariate Statistics by : Martin Bilodeau
Download or read book Theory of Multivariate Statistics written by Martin Bilodeau and published by Springer Science & Business Media. This book was released on 2008-01-20 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended as a textbook for students taking a first graduate course in the subject, as well as for the general reference of interested research workers, this text discusses, in a readable form, developments from recently published work on certain broad topics not otherwise easily accessible, such as robust inference and the use of the bootstrap in a multivariate setting. A minimum background expected of the reader would include at least two courses in mathematical statistics, and certainly some exposure to the calculus of several variables together with the descriptive geometry of linear algebra.
Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Book Synopsis Synopses for Massive Data by : Graham Cormode
Download or read book Synopses for Massive Data written by Graham Cormode and published by Now Publishers. This book was released on 2012 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes basic principles and recent developments in approximate query processing. It focuses on four key synopses: random samples, histograms, wavelets, and sketches. It considers issues such as accuracy, space and time efficiency, optimality, practicality, range of applicability, error bounds on query answers, and incremental maintenance.
Book Synopsis An Introduction to Applied Multivariate Analysis with R by : Brian Everitt
Download or read book An Introduction to Applied Multivariate Analysis with R written by Brian Everitt and published by Springer Science & Business Media. This book was released on 2011-04-23 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
