Linear Models For Unbalanced Data

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Linear Models for Unbalanced Data

Author : Shayle R. Searle
Publisher : John Wiley & Sons
ISBN 13 : 0470040041
Total Pages : 570 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Linear Models for Unbalanced Data by : Shayle R. Searle

Download or read book Linear Models for Unbalanced Data written by Shayle R. Searle and published by John Wiley & Sons. This book was released on 2006-03-17 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Analysis of Variance, Design, and Regression

Author : Ronald Christensen
Publisher : CRC Press
ISBN 13 : 1498730191
Total Pages : 453 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Analysis of Variance, Design, and Regression by : Ronald Christensen

Download or read book Analysis of Variance, Design, and Regression written by Ronald Christensen and published by CRC Press. This book was released on 2018-09-03 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data. New to the Second Edition Reorganized to focus on unbalanced data Reworked balanced analyses using methods for unbalanced data Introductions to nonparametric and lasso regression Introductions to general additive and generalized additive models Examination of homologous factors Unbalanced split plot analyses Extensions to generalized linear models R, Minitab®, and SAS code on the author’s website The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.

Linear Models

Author : Shayle R. Searle
Publisher : John Wiley & Sons
ISBN 13 : 1118952847
Total Pages : 696 pages
Book Rating : 4.1/5 (189 download)

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Book Synopsis Linear Models by : Shayle R. Searle

Download or read book Linear Models written by Shayle R. Searle and published by John Wiley & Sons. This book was released on 2016-09-23 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Analysis of Variance, Design, and Regression

Author : Ronald Christensen
Publisher : CRC Press
ISBN 13 : 9780412062919
Total Pages : 608 pages
Book Rating : 4.0/5 (629 download)

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Book Synopsis Analysis of Variance, Design, and Regression by : Ronald Christensen

Download or read book Analysis of Variance, Design, and Regression written by Ronald Christensen and published by CRC Press. This book was released on 1996-06-01 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents a comprehensive treatment of basic statistical methods and their applications. It focuses on the analysis of variance and regression, but also addressing basic ideas in experimental design and count data. The book has four connecting themes: similarity of inferential procedures, balanced one-way analysis of variance, comparison of models, and checking assumptions. Most inferential procedures are based on identifying a scalar parameter of interest, estimating that parameter, obtaining the standard error of the estimate, and identifying the appropriate reference distribution. Given these items, the inferential procedures are identical for various parameters. Balanced one-way analysis of variance has a simple, intuitive interpretation in terms of comparing the sample variance of the group means with the mean of the sample variance for each group. All balanced analysis of variance problems are considered in terms of computing sample variances for various group means. Comparing different models provides a structure for examining both balanced and unbalanced analysis of variance problems and regression problems. Checking assumptions is presented as a crucial part of every statistical analysis. Examples using real data from a wide variety of fields are used to motivate theory. Christensen consistently examines residual plots and presents alternative analyses using different transformation and case deletions. Detailed examination of interactions, three factor analysis of variance, and a split-plot design with four factors are included. The numerous exercises emphasize analysis of real data. Senior undergraduate and graduate students in statistics and graduate students in other disciplines using analysis of variance, design of experiments, or regression analysis will find this book useful.

Linear Models in Statistics

Author : Alvin C. Rencher
Publisher : John Wiley & Sons
ISBN 13 : 0470192607
Total Pages : 690 pages
Book Rating : 4.4/5 (71 download)

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Book Synopsis Linear Models in Statistics by : Alvin C. Rencher

Download or read book Linear Models in Statistics written by Alvin C. Rencher and published by John Wiley & Sons. This book was released on 2008-01-07 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

The Analysis of Linear Models

Author : Ronald R. Hocking
Publisher : Thomson Brooks/Cole
ISBN 13 :
Total Pages : 414 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis The Analysis of Linear Models by : Ronald R. Hocking

Download or read book The Analysis of Linear Models written by Ronald R. Hocking and published by Thomson Brooks/Cole. This book was released on 1985 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Linear Models

Author : Shayle R. Searle
Publisher : John Wiley & Sons
ISBN 13 : 1118952839
Total Pages : 682 pages
Book Rating : 4.1/5 (189 download)

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Book Synopsis Linear Models by : Shayle R. Searle

Download or read book Linear Models written by Shayle R. Searle and published by John Wiley & Sons. This book was released on 2016-10-31 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Regression and Classification

Author : Norman Matloff
Publisher : CRC Press
ISBN 13 : 1351645897
Total Pages : 516 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Statistical Regression and Classification by : Norman Matloff

Download or read book Statistical Regression and Classification written by Norman Matloff and published by CRC Press. This book was released on 2017-09-19 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.

SAS for Linear Models

Author : Ramon Littell
Publisher : John Wiley & Sons
ISBN 13 : 0471221740
Total Pages : 500 pages
Book Rating : 4.4/5 (712 download)

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Book Synopsis SAS for Linear Models by : Ramon Littell

Download or read book SAS for Linear Models written by Ramon Littell and published by John Wiley & Sons. This book was released on 2002-05-24 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning from Imbalanced Data Sets

Author : Alberto Fernández
Publisher : Springer
ISBN 13 : 3319980742
Total Pages : 377 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Learning from Imbalanced Data Sets by : Alberto Fernández

Download or read book Learning from Imbalanced Data Sets written by Alberto Fernández and published by Springer. This book was released on 2018-10-22 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

Linear Model Methodology

Author : Andre I. Khuri
Publisher : CRC Press
ISBN 13 : 1420010441
Total Pages : 562 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Linear Model Methodology by : Andre I. Khuri

Download or read book Linear Model Methodology written by Andre I. Khuri and published by CRC Press. This book was released on 2009-10-21 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Linear Models

Author : William R. Moser
Publisher : Elsevier
ISBN 13 : 9780080510293
Total Pages : 228 pages
Book Rating : 4.5/5 (12 download)

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Book Synopsis Linear Models by : William R. Moser

Download or read book Linear Models written by William R. Moser and published by Elsevier. This book was released on 1996-10-18 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear models, normally presented in a highly theoretical and mathematical style, are brought down to earth in this comprehensive textbook. Linear Models examines the subject from a mean model perspective, defining simple and easy-to-learn rules for building mean models, regression models, mean vectors, covariance matrices and sums of squares matrices for balanced and unbalanced data sets. The author includes both applied and theoretical discussions of the multivariate normal distribution, quadratic forms, maximum likelihood estimation, less than full rank models, and general mixed models. The mean model is used to bring all of these topics together in a coherent presentation of linear model theory. Provides a versatile format for investigating linear model theory, using the mean model Uses examples that are familiar to the student: Design of experiments, analysis of variance, regression, and normal distribution theory Includes a review of relevant linear algebra concepts Contains fully worked examples which follow the theorem/proof presentation

Linear Models

Author : Shayle R. Searle
Publisher : John Wiley & Sons
ISBN 13 : 0471184993
Total Pages : 565 pages
Book Rating : 4.4/5 (711 download)

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Book Synopsis Linear Models by : Shayle R. Searle

Download or read book Linear Models written by Shayle R. Searle and published by John Wiley & Sons. This book was released on 1997-03-28 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Imbalanced Learning

Author : Haibo He
Publisher : John Wiley & Sons
ISBN 13 : 1118646339
Total Pages : 222 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Imbalanced Learning by : Haibo He

Download or read book Imbalanced Learning written by Haibo He and published by John Wiley & Sons. This book was released on 2013-06-07 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.

Methods and Applications of Linear Models

Author : Ronald R. Hocking
Publisher : John Wiley & Sons
ISBN 13 : 1118593022
Total Pages : 724 pages
Book Rating : 4.1/5 (185 download)

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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.

Statistical Tests for Mixed Linear Models

Author : André I. Khuri
Publisher : John Wiley & Sons
ISBN 13 : 1118164857
Total Pages : 384 pages
Book Rating : 4.1/5 (181 download)

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Book Synopsis Statistical Tests for Mixed Linear Models by : André I. Khuri

Download or read book Statistical Tests for Mixed Linear Models written by André I. Khuri and published by John Wiley & Sons. This book was released on 2011-09-09 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Linear Models and Generalizations

Author : C. Radhakrishna Rao
Publisher : Springer Science & Business Media
ISBN 13 : 3540742271
Total Pages : 583 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Linear Models and Generalizations by : C. Radhakrishna Rao

Download or read book Linear Models and Generalizations written by C. Radhakrishna Rao and published by Springer Science & Business Media. This book was released on 2007-10-15 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Revised and updated with the latest results, this Third Edition explores the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations. Highlights of coverage include sensitivity analysis and model selection, an analysis of incomplete data, an analysis of categorical data based on a unified presentation of generalized linear models, and an extensive appendix on matrix theory.