Variance Components

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. ". . .Variance Components is an excellent book. It is organized and well written, and provides many references to a variety of topics. I recommend it to anyone with interest in linear models." ―Journal of the American Statistical Association "This book provides a broad coverage of methods for estimating variance components which appeal to students and research workers . . . The authors make an outstanding contribution to teaching and research in the field of variance component estimation." ―Mathematical Reviews "The authors have done an excellent job in collecting materials on a broad range of topics. Readers will indeed gain from using this book . . . I must say that the authors have done a commendable job in their scholarly presentation." ―Technometrics This book focuses on summarizing the variability of statistical data known as the analysis of variance table. Penned in a readable style, it provides an up-to-date treatment of research in the area. The book begins with the history of analysis of variance and continues with discussions of balanced data, analysis of variance for unbalanced data, predictions of random variables, hierarchical models and Bayesian estimation, binary and discrete data, and the dispersion mean model.

Science & Math

Searle, Shayle R.

Mathematics

Wiley-Interscience

Matrix Algebra Useful for Statistics

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected booksthat have been made more accessible to consumers in an effort toincrease global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists. "This book is intended to teach useful matrix algebra to 'students,teachers, consultants, researchers, and practitioners' in'statistics and other quantitative methods'.The author concentrateson practical matters, and writes in a friendly and informal style .. . this is a useful and enjoyable book to have at hand." -Biometrics This book is an easy-to-understand guide to matrix algebra and itsuses in statistical analysis. The material is presented in anexplanatory style rather than the formal theorem-proof format. Thisself-contained text includes numerous applied illustrations,numerical examples, and exercises.

Linear Models for Unbalanced Data

WILEY-INTERSCIENCE PAPERBACK SERIES  The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.  "[This book] provides an excellent discussion of the methodology and interpretation of linear models analysis of unbalanced data (data having unequal numbers of observations in the subclasses), generally without matrices?the author does an excellent job of emphasizing the more practical nature of the book. Highly recommended for graduate and undergraduate libraries." â?"Choice  "This is a very comprehensive text, aimed at both students studying linear-model theory and practicing statisticians who require an understanding of the model-fitting procedures incorporated in statistical packages?This book should be considered as a text for college courses as it provides a clearly presented and thorough treatment of linear models. It will also be useful to any practicing statistician who has to analyze unbalanced data, perhaps arising from surveys, and wishes to understand the output from model-fitting procedures and the discrepancies in analysis from one recognized package to another." â?"Biometrics  This newly available and affordably priced paperback version of Linear Models for Unbalanced Data offers a presentation of the fundamentals of linear statistical models unique in its total devotion to unbalanced data and its emphasis on the up-to-date cell means model approach to linear models for unbalanced data. Topic coverage includes cell means models, 1-way classification, nested classifications, 2-way classification with some-cells-empty data, models with covariables, matrix algebra and quadratic forms, linear model theory, and much more.

An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects. A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed models is maintained throughout, and each chapter illustrates how these models are applicable in a wide array of contexts. In addition, a discussion of general methods for the analysis of such models is presented with an emphasis on the method of maximum likelihood for the estimation of parameters. The authors also provide comprehensive coverage of the latest statistical models for correlated, non-normally distributed data. Thoroughly updated to reflect the latest developments in the field, the Second Edition features: <ul>
<li>A new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimation</li> <li>A new chapter that treats shared random effects models, latent class models, and properties of models</li> <li>A revised chapter on longitudinal data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and within covariate decompositions</li> <li>Expanded coverage of marginal versus conditional models</li> <li>Numerous new and updated examples</li> </ul>
 With its accessible style and wealth of illustrative exercises, Generalized, Linear, and Mixed Models, Second Edition is an ideal book for courses on generalized linear and mixed models at the upper-undergraduate and beginning-graduate levels. It also serves as a valuable reference for applied statisticians, industrial practitioners, and researchers.

Generalized, Linear, and Mixed Models

McCulloch, Charles E.

Neuhaus, John M.

This set features: Variance Components by Shayle R. Searle, George Casella, Charles E. McCulloch (978-0-470-00959-8) Matrix Algebra for Useful Statistics by Shayle R. Searle (978-0-470-00961-1) Linear Models for Unbalanced Data by Shayle R. Searle (978-0-470-04004-1) Generalized, Linear, and Mixed Models, Second Edition by Charles E. McCulloch, Shayle R. Searle, John M. Neuhaus (978-0-470-07371-1) Linear Models by Shayle R. Searle (978-0-471-18499-7)

The Collected Works of Shayle R. Searle (Wiley Series in Probability and Statistics)

wiley

This 1971 classic on linear models is once again available--as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.

Linear Models

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

Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics)

Politics & Social Sciences

Sociology

Coverage of matrix algebra for economists and students ofeconomics Matrix Algebra for Applied Economics explains the important tool ofmatrix algebra for students of economics and practicing economists.It includes examples that demonstrate the foundation operations ofmatrix algebra and illustrations of using the algebra for a varietyof economic problems. The authors present the scope and basic definitions of matrices,their arithmetic and simple operations, and describe specialmatrices and their properties, including the analog of division.They provide in-depth coverage of necessary theory and deal withconcepts and operations for using matrices in real-life situations.They discuss linear dependence and independence, as well as rank,canonical forms, generalized inverses, eigenroots, and vectors.Topics of prime interest to economists are shown to be simplifiedusing matrix algebra in linear equations, regression, linearmodels, linear programming, and Markov chains. Highlights include: * Numerous examples of real-world applications * Challenging exercises throughout the book * Mathematics understandable to readers of all backgrounds * Extensive up-to-date reference material Matrix Algebra for Applied Economics provides excellent guidancefor advanced undergraduate students and also graduate students.Practicing economists who want to sharpen their skills will findthis book both practical and easy-to-read, no matter what theirapplied interests.

Matrix Algebra for Applied Economics

Business & Money

Willett, Lois Schertz

Economics

Linear Models (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section)

This presentation of the fundamentals of linear statistical models is unique in its total devotion to unbalanced data (data having unequal numbers of observations in the subclasses) and its emphasis on the up-to-date cell means model approach to linear models for unbalanced data. Unbalanced data are harder to analyze and understand than balanced data, but are becoming more prevalent through computer storage of data. Topics covered include cell means models, 1-way classification, nested classifications, 2-way classification with some-cells-empty data, models with covariables, matrix algebra and quadratic forms, linear model theory, comments on computing packages, and much more. Includes references and statistical tables.

See the best price to sell, buy, or rent books by Searle, Shayle R.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Searle, Shayle R.

See the best price to sell, buy, or rent books by Searle, Shayle R.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Variance Components

Matrix Algebra Useful for Statistics

Linear Models for Unbalanced Data

Generalized, Linear, and Mixed Models

The Collected Works of Shayle R. Searle (Wiley Series in Probability and Statistics)

Linear Models

Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics)

Matrix Algebra for Applied Economics

Linear Models (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section)

Linear Models for Unbalanced Data