Linear Models For Unbalanced Data
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Linear Models For Unbalanced Data
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Author : Shayle R. Searle
language : en
Publisher: John Wiley & Sons
Release Date : 2006-03-17
Linear Models For Unbalanced Data written by Shayle R. Searle and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-17 with Mathematics categories.
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.
Linear Models In Statistics
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Author : Mr. Rohit Manglik
language : en
Publisher: EduGorilla Publication
Release Date : 2024-07-26
Linear Models In Statistics written by Mr. Rohit Manglik and has been published by EduGorilla Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-26 with Mathematics categories.
EduGorilla Publication is a trusted name in the education sector, committed to empowering learners with high-quality study materials and resources. Specializing in competitive exams and academic support, EduGorilla provides comprehensive and well-structured content tailored to meet the needs of students across various streams and levels.
Linear Models
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Author : Shayle R. Searle
language : en
Publisher: John Wiley & Sons
Release Date : 2016-10-31
Linear Models written by Shayle R. Searle and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-31 with Mathematics categories.
Provides an easy-to-understand guide to statistical linear models and its uses in data analysis This book defines a broad spectrum of statistical linear models that is useful in the analysis of data. Considerable rewriting was done to make the book more reader friendly than the first edition. Linear Models, Second Edition is written in such a way as to be self-contained for a person with a background in basic statistics, calculus and linear algebra. The text includes numerous applied illustrations, numerical examples, and exercises, now augmented with computer outputs in SAS and R. Also new to this edition is: • A greatly improved internal design and format • A short introductory chapter to ease understanding of the order in which topics are taken up • Discussion of additional topics including multiple comparisons and shrinkage estimators • Enhanced discussions of generalized inverses, the MINQUE, Bayes and Maximum Likelihood estimators for estimating variance components Furthermore, in this edition, the second author adds many pedagogical elements throughout the book. These include numbered examples, end-of-example and end-of-proof symbols, selected hints and solutions to exercises available on the book’s website, and references to “big data” in everyday life. Featuring a thorough update, Linear Models, Second Edition includes: • A new internal format, additional instructional pedagogy, selected hints and solutions to exercises, and several more real-life applications • Many examples using SAS and R with timely data sets • Over 400 examples and exercises throughout the book to reinforce understanding Linear Models, Second Edition is a textbook and a reference for upper-level undergraduate and beginning graduate-level courses on linear models, statisticians, engineers, and scientists who use multiple regression or analysis of variance in their work. SHAYLE R. SEARLE, PhD, was Professor Emeritus of Biometry at Cornell University. He was the author of the first edition of Linear Models, Linear Models for Unbalanced Data, and Generalized, Linear, and Mixed Models (with Charles E. McCulloch), all from Wiley. The first edition of Linear Models appears in the Wiley Classics Library. MARVIN H. J. GRUBER, PhD, is Professor Emeritus at Rochester Institute of Technology, School of Mathematical Sciences. Dr. Gruber has written a number of papers and has given numerous presentations at professional meetings during his tenure as a professor at RIT. His fields of interest include regression estimators and the improvement of their efficiency using shrinkage estimators. He has written and published two books on this topic. Another of his books, Matrix Algebra for Linear Models, also published by Wiley, provides good preparation for studying Linear Models. He is a member of the American Mathematical Society, the Institute of Mathematical Statistics and the American Statistical Association.
Linear Models In Statistics
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Author : Alvin C. Rencher
language : en
Publisher: John Wiley & Sons
Release Date : 2008-01-07
Linear Models In Statistics written by Alvin C. Rencher and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-07 with Mathematics categories.
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
Linear Models And Generalizations
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Author : C. Radhakrishna Rao
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-15
Linear Models And Generalizations written by C. Radhakrishna Rao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-15 with Mathematics categories.
Thebookisbasedonseveralyearsofexperienceofbothauthorsinteaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and o?ers a selectionofclassicalandmodernalgebraicresultsthatareusefulinresearch work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results aboutthe de?niteness ofmatrices,especially forthe di?erences ofmatrices, which enable superiority comparisons of two biased estimates to be made for the ?rst time. We have attempted to provide a uni?ed theory of inference from linear models with minimal assumptions. Besides the usual least-squares theory, alternative methods of estimation and testing based on convex loss fu- tions and general estimating equations are discussed. Special emphasis is given to sensitivity analysis and model selection. A special chapter is devoted to the analysis of categorical data based on logit, loglinear, and logistic regression models. The material covered, theoretical discussion, and a variety of practical applications will be useful not only to students but also to researchers and consultants in statistics.
Linear Model Methodology
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Author : Andre I. Khuri
language : en
Publisher: CRC Press
Release Date : 2009-10-21
Linear Model Methodology written by Andre I. Khuri and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-21 with Mathematics categories.
Given the importance of linear models in statistical theory and experimental research, a good understanding of their fundamental principles and theory is essential. Supported by a large number of examples, Linear Model Methodology provides a strong foundation in the theory of linear models and explores the latest developments in data analysis.After
Sas For Linear Models
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Author : Ramon Littell
language : en
Publisher: John Wiley & Sons
Release Date : 2002-05-24
Sas For Linear Models written by Ramon Littell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-05-24 with Mathematics categories.
Features and capabilities of the REG, ANOVA, and GLM procedures are included in this introduction to analysing linear models with the SAS System. This guide shows how to apply the appropriate procedure to data analysis problems and understand PROC GLM output. Other helpful guidelines and discussions cover the following significant areas: Multivariate linear models; lack-of-fit analysis; covariance and heterogeneity of slopes; a classification with both crossed and nested effects; and analysis of variance for balanced data. This fourth edition includes updated examples, new software-related features, and new material, including a chapter on generalised linear models. Version 8 of the SAS System was used to run the SAS code examples in the book. * Provides clear explanations of how to use SAS to analyse linear models * Includes numerous SAS outputs * Includes new chapter on generalised linear models * Uses version 8 of the SAS system This book assists data analysts who use SAS/STAT software to analyse data using regression analysis and analysis of variance. It assumes familiarity with basic SAS concepts such as creating SAS data sets with the DATA step and manipulating SAS data sets with the procedures in base SAS software.
Methods And Applications Of Linear Models
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Author : Ronald R. Hocking
language : en
Publisher: John Wiley & Sons
Release Date : 2005-02-04
Methods And Applications Of Linear Models written by Ronald R. Hocking and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-04 with Mathematics categories.
A popular statistical text now updated and better than ever! The ready availability of high-speed computers and statistical software encourages the analysis of ever larger and more complex problems while at the same time increasing the likelihood of improper usage. That is why it is increasingly important to educate end users in the correct interpretation of the methodologies involved. Now in its second edition, Methods and Applications of Linear Models: Regression and the Analysis of Variance seeks to more effectively address the analysis of such models through several important changes. Notable in this new edition: Fully updated and expanded text reflects the most recent developments in the AVE method Rearranged and reorganized discussions of application and theory enhance text’s effectiveness as a teaching tool More than 100 new exercises in the areas of regression and analysis of variance As in the First Edition, the author presents a thorough treatment of the concepts and methods of linear model analysis, and illustrates them with various numerical and conceptual examples, using a data-based approach to development and analysis. Data sets, available on an FTP site, allow readers to apply analytical methods discussed in the book.
Statistical Tests For Mixed Linear Models
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Author : André I. Khuri
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-09
Statistical Tests For Mixed Linear Models written by André I. Khuri and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-09 with Mathematics categories.
An advanced discussion of linear models with mixed or randomeffects. In recent years a breakthrough has occurred in our ability todraw inferences from exact and optimum tests of variance componentmodels, generating much research activity that relies on linearmodels with mixed and random effects. This volume covers the mostimportant research of the past decade as well as the latestdevelopments in hypothesis testing. It compiles all currentlyavailable results in the area of exact and optimum tests forvariance component models and offers the only comprehensivetreatment for these models at an advanced level. Statistical Tests for Mixed Linear Models: Combines analysis and testing in one self-containedvolume. Describes analysis of variance (ANOVA) procedures in balancedand unbalanced data situations. Examines methods for determining the effect of imbalance ondata analysis. Explains exact and optimum tests and methods for theirderivation. Summarizes test procedures for multivariate mixed and randommodels. Enables novice readers to skip the derivations and discussionson optimum tests. Offers plentiful examples and exercises, manyof which are numerical in flavor. Provides solutions to selected exercises. Statistical Tests for Mixed Linear Models is an accessiblereference for researchers in analysis of variance, experimentaldesign, variance component analysis, and linear mixed models. It isalso an important text for graduate students interested in mixedmodels.
Compstat 1982 5th Symposium Held At Toulouse 1982
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Author : H. Caussinus
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Compstat 1982 5th Symposium Held At Toulouse 1982 written by H. Caussinus and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Pour Ie cinquieme congres de la serie, COMPSTAT 82 reunit environ 500 participants d'origines scientifiques et geographiques tres variees, prouvant a l'evidence l'interet persis tant de la communaute scientifique pour tous les problemes de calculs statistiques. Le Comite de Programme charge de l'organisation scientifique du Congres etait com pose de: o S. Apelt (Republique democratique d'Allemagne) - A. Bj6rck (Suede) - H. Caussinus (France), President - Y. Escoufier (France) - A. de Falguerolles (France), Secretaire - J.W. Frane (U.S.A.) - J. Gordesch (Republique Federale d'Allemagne) - Th. Havranek (Tchechoslovaquie) - N. Lauro (Italie) - C. Millier (France) - R.J. Mokken (pays-Bas)- R. Tomassone (France) - D. Wishart (Royaume Uni) Ce Comite a decide d'augmenter Ie nombre des conferenciers invites, cherchant de la sorte une representation des diverses ecoles ainsi que l'introduction de nouveaux themes. La tache la plus difficile a ensuite ete de selectionner une soixantaine de contributions parmi 250 soumissions. La encore Ie Comite de Programme s'est efforce de favoriser des voies qui semblaient les plus nouvelles et a essaye de maintenir une bonne repartition scientifique et geographique. Cependant, comme dans les precedents congres COMPSTAT, il a donne la preference aux propositions clairement marquees simultanement du double aspect Statistique et Calcul. Dans bien des cas, ces deux aspects sont tres lies rendant en particulier difficile et peu pertinente toute classification fine des contributions.