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Author : Bengt Berlin
Publisher :
ISBN 13 :
Total Pages : 38 pages
Book Rating : 4.:/5 (29 download)
Download or read book Approximate Confidence Intervals for the Main Effects in an Unbalanced, Mixed Model written by Bengt Berlin and published by . This book was released on 1974 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Richard K. Burdick
Publisher : CRC Press
ISBN 13 : 148227714X
Total Pages : 228 pages
Book Rating : 4.4/5 (822 download)
Download or read book Confidence Intervals on Variance Components written by Richard K. Burdick and published by CRC Press. This book was released on 1992-02-28 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summarizes information scattered in the technical literature on a subject too new to be included in most textbooks, but which is of interest to statisticians, and those who use statistics in science and education, at an advanced undergraduate or higher level. Overviews recent research on constructin
Author : Hardeo Sahai
Publisher : Springer Science & Business Media
ISBN 13 : 0817644253
Total Pages : 493 pages
Book Rating : 4.8/5 (176 download)
Download or read book Analysis of Variance for Random Models, Volume 2: Unbalanced Data written by Hardeo Sahai and published by Springer Science & Business Media. This book was released on 2007-07-03 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Systematic treatment of the commonly employed crossed and nested classification models used in analysis of variance designs with a detailed and thorough discussion of certain random effects models not commonly found in texts at the introductory or intermediate level. It also includes numerical examples to analyze data from a wide variety of disciplines as well as any worked examples containing computer outputs from standard software packages such as SAS, SPSS, and BMDP for each numerical example.
Author : Lang Wu
Publisher : CRC Press
ISBN 13 : 9781420074086
Total Pages : 431 pages
Book Rating : 4.0/5 (74 download)
Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
Author : Walter W. Stroup
Publisher : SAS Institute
ISBN 13 : 163526152X
Total Pages : 823 pages
Book Rating : 4.6/5 (352 download)
Download or read book SAS for Mixed Models written by Walter W. Stroup and published by SAS Institute. This book was released on 2018-12-12 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.
Author : Marc S. Paolella
Publisher : John Wiley & Sons
ISBN 13 : 1119431859
Total Pages : 900 pages
Book Rating : 4.1/5 (194 download)
Download or read book Linear Models and Time-Series Analysis written by Marc S. Paolella and published by John Wiley & Sons. This book was released on 2018-10-10 with total page 900 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.
Author : Hardeo Sahai
Publisher : Springer Science & Business Media
ISBN 13 : 081768168X
Total Pages : 499 pages
Book Rating : 4.8/5 (176 download)
Download or read book Analysis of Variance for Random Models written by Hardeo Sahai and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: ANOVA models involving random effects have found widespread application to experimental design in varied fields such as biology, econometrics, and engineering. Volume I of this two-part work is a comprehensive presentation of methods and techniques for point estimation, interval estimation, and hypotheses tests for linear models involving random effects. Volume I examines models with balanced data (orthogonal models); Volume II studies models with unbalanced data (non-orthogonal models). Accessible to readers with a modest mathematical and statistical background, the work will appeal to a broad audience of graduate students, researchers, and practitioners. It can be used as a graduate text or as a self-study reference.
Author : André I. Khuri
Publisher : John Wiley & Sons
ISBN 13 : 1118164857
Total Pages : 384 pages
Book Rating : 4.1/5 (181 download)
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.
Author : Hardeo Sahai
Publisher : Springer Science & Business Media
ISBN 13 : 1461213444
Total Pages : 766 pages
Book Rating : 4.4/5 (612 download)
Download or read book The Analysis of Variance written by Hardeo Sahai and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 766 pages. Available in PDF, EPUB and Kindle. Book excerpt: The analysis of variance (ANOYA) models have become one of the most widely used tools of modern statistics for analyzing multifactor data. The ANOYA models provide versatile statistical tools for studying the relationship between a dependent variable and one or more independent variables. The ANOYA mod els are employed to determine whether different variables interact and which factors or factor combinations are most important. They are appealing because they provide a conceptually simple technique for investigating statistical rela tionships among different independent variables known as factors. Currently there are several texts and monographs available on the sub ject. However, some of them such as those of Scheffe (1959) and Fisher and McDonald (1978), are written for mathematically advanced readers, requiring a good background in calculus, matrix algebra, and statistical theory; whereas others such as Guenther (1964), Huitson (1971), and Dunn and Clark (1987), although they assume only a background in elementary algebra and statistics, treat the subject somewhat scantily and provide only a superficial discussion of the random and mixed effects analysis of variance.
Author : Shayle R. Searle
Publisher : John Wiley & Sons
ISBN 13 : 0470317698
Total Pages : 537 pages
Book Rating : 4.4/5 (73 download)
Download or read book Variance Components written by Shayle R. Searle and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 537 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. ". . .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.
Author : Ravindra Khattree
Publisher : Gulf Professional Publishing
ISBN 13 : 9780444506146
Total Pages : 1222 pages
Book Rating : 4.5/5 (61 download)
Download or read book Statistics in Industry written by Ravindra Khattree and published by Gulf Professional Publishing. This book was released on 2003-07-18 with total page 1222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents an exposition of topics in industrial statistics. It serves as a reference for researchers in industrial statistics/industrial engineering and a source of information for practicing statisticians/industrial engineers. A variety of topics in the areas of industrial process monitoring, industrial experimentation, industrial modelling and data analysis are covered and are authored by leading researchers or practitioners in the particular specialized topic. Targeting the audiences of researchers in academia as well as practitioners and consultants in industry, the book provides comprehensive accounts of the relevant topics. In addition, whenever applicable ample data analytic illustrations are provided with the help of real world data.
Author : Ronald H Heck
Publisher : Routledge
ISBN 13 : 1136672346
Total Pages : 449 pages
Book Rating : 4.1/5 (366 download)
Download or read book Multilevel Modeling of Categorical Outcomes Using IBM SPSS written by Ronald H Heck and published by Routledge. This book was released on 2013-05-07 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced. Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors’ highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.
Author : Charles E. McCulloch
Publisher : John Wiley & Sons
ISBN 13 : 1118209966
Total Pages : 281 pages
Book Rating : 4.1/5 (182 download)
Download or read book Generalized, Linear, and Mixed Models written by Charles E. McCulloch and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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: A new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimation A new chapter that treats shared random effects models, latent class models, and properties of models 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 Expanded coverage of marginal versus conditional models Numerous new and updated examples 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.
Author : Julian J. Faraway
Publisher : CRC Press
ISBN 13 : 1498720986
Total Pages : 399 pages
Book Rating : 4.4/5 (987 download)
Download or read book Extending the Linear Model with R written by Julian J. Faraway and published by CRC Press. This book was released on 2016-03-23 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics. New to the Second Edition Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available Updated coverage of splines and confidence bands in the chapter on nonparametric regression New material on random forests for regression and classification Revamped R code throughout, particularly the many plots using the ggplot2 package Revised and expanded exercises with solutions now included Demonstrates the Interplay of Theory and Practice This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.
Author : Edward E. Gbur
Publisher : John Wiley & Sons
ISBN 13 : 0891181822
Total Pages : 304 pages
Book Rating : 4.8/5 (911 download)
Download or read book Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences written by Edward E. Gbur and published by John Wiley & Sons. This book was released on 2020-01-22 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences provides readers with an understanding and appreciation for the design and analysis of mixed models for non-normally distributed data. It is the only publication of its kind directed specifically toward the agricultural and natural resources sciences audience. Readers will especially benefit from the numerous worked examples based on actual experimental data and the discussion of pitfalls associated with incorrect analyses.
Author : Richard K. Burdick
Publisher : SIAM
ISBN 13 : 9780898718379
Total Pages : 219 pages
Book Rating : 4.7/5 (183 download)
Download or read book Design and Analysis of Gauge R&R Studies written by Richard K. Burdick and published by SIAM. This book was released on 2005-01-01 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a protocol for conducting gauge repeatability and reproducibility (R&R) experiments. Such an experiment is required whenever a new test system is developed to monitor a manufacturing process. The protocol presented here is used to determine if the testing system is capable of monitoring the manufacturing process with the desired level of accuracy and precision. This protocol is not currently available in other books or technical reports. In addition to providing a protocol for testing a measurement system, the book presents an up-to-date summary of methods used to construct confidence intervals in normal-based random and mixed analysis of variance (ANOVA) models. Thus, this comprehensive book will be useful to scientists in all fields of application who wish to construct interval estimates for ANOVA model parameters. It includes approaches that can be applied to any ANOVA model, and because it contains detailed examples of all computations, practitioners will be able to easily apply the methods. The book describes methods for constructing two types of confidence intervals: modified large-sample (MLS) and generalized confidence intervals. Computer codes written in SAS and Excel are provided to perform the computations. Appendices are included for readers who are unfamiliar with confidence intervals or lack a basic understanding of random and mixed ANOVA models.
Author : Walter W. Stroup
Publisher : CRC Press
ISBN 13 : 1439815135
Total Pages : 547 pages
Book Rating : 4.4/5 (398 download)
Download or read book Generalized Linear Mixed Models written by Walter W. Stroup and published by CRC Press. This book was released on 2016-04-19 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.
