On Some Extensions Of Generalized Linear Models With Varying Dispersion

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Applying Generalized Linear Models

Author : James K. Lindsey
Publisher : Springer Science & Business Media
ISBN 13 : 038722730X
Total Pages : 265 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Applying Generalized Linear Models by : James K. Lindsey

Download or read book Applying Generalized Linear Models written by James K. Lindsey and published by Springer Science & Business Media. This book was released on 2008-01-15 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non- statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

Generalized Linear Models

Author : P. McCullagh
Publisher : Routledge
ISBN 13 : 1351445847
Total Pages : 536 pages
Book Rating : 4.3/5 (514 download)

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Book Synopsis Generalized Linear Models by : P. McCullagh

Download or read book Generalized Linear Models written by P. McCullagh and published by Routledge. This book was released on 2019-01-22 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and ot

Generalized Linear Models and Extensions, Second Edition

Author : James W. Hardin
Publisher : Stata Press
ISBN 13 : 1597180149
Total Pages : 413 pages
Book Rating : 4.5/5 (971 download)

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Book Synopsis Generalized Linear Models and Extensions, Second Edition by : James W. Hardin

Download or read book Generalized Linear Models and Extensions, Second Edition written by James W. Hardin and published by Stata Press. This book was released on 2007 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deftly balancing theory and application, this book stands out in its coverage of the derivation of the GLM families and their foremost links. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, and more.

Foundations of Linear and Generalized Linear Models

Author : Alan Agresti
Publisher : John Wiley & Sons
ISBN 13 : 1118730038
Total Pages : 471 pages
Book Rating : 4.1/5 (187 download)

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Book Synopsis Foundations of Linear and Generalized Linear Models by : Alan Agresti

Download or read book Foundations of Linear and Generalized Linear Models written by Alan Agresti and published by John Wiley & Sons. This book was released on 2015-02-23 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Linear Models with R

Author : Julian J. Faraway
Publisher : CRC Press
ISBN 13 : 1439887349
Total Pages : 284 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Linear Models with R by : Julian J. Faraway

Download or read book Linear Models with R written by Julian J. Faraway and published by CRC Press. This book was released on 2016-04-19 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models

Generalized Linear Models for Bounded and Limited Quantitative Variables

Author : Michael Smithson
Publisher : SAGE Publications
ISBN 13 : 1544334540
Total Pages : 137 pages
Book Rating : 4.5/5 (443 download)

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Book Synopsis Generalized Linear Models for Bounded and Limited Quantitative Variables by : Michael Smithson

Download or read book Generalized Linear Models for Bounded and Limited Quantitative Variables written by Michael Smithson and published by SAGE Publications. This book was released on 2019-09-09 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces researchers and students to the concepts and generalized linear models for analyzing quantitative random variables that have one or more bounds. Examples of bounded variables include the percentage of a population eligible to vote (bounded from 0 to 100), or reaction time in milliseconds (bounded below by 0). The human sciences deal in many variables that are bounded. Ignoring bounds can result in misestimation and improper statistical inference. Michael Smithson and Yiyun Shou′s book brings together material on the analysis of limited and bounded variables that is scattered across the literature in several disciplines, and presents it in a style that is both more accessible and up-to-date. The authors provide worked examples in each chapter using real datasets from a variety of disciplines. The software used for the examples include R, SAS, and Stata. The data, software code, and detailed explanations of the example models are available on an accompanying website.

Generalized Linear Models for Insurance Rating

Author : Mark Goldburd
Publisher :
ISBN 13 : 9780996889728
Total Pages : 106 pages
Book Rating : 4.8/5 (897 download)

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Book Synopsis Generalized Linear Models for Insurance Rating by : Mark Goldburd

Download or read book Generalized Linear Models for Insurance Rating written by Mark Goldburd and published by . This book was released on 2016-06-08 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Longitudinal and Panel Data

Author : Edward W. Frees
Publisher : Cambridge University Press
ISBN 13 : 9780521535380
Total Pages : 492 pages
Book Rating : 4.5/5 (353 download)

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Book Synopsis Longitudinal and Panel Data by : Edward W. Frees

Download or read book Longitudinal and Panel Data written by Edward W. Frees and published by Cambridge University Press. This book was released on 2004-08-16 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.

New Statistics for Design Researchers

Author : Martin Schmettow
Publisher : Springer
ISBN 13 : 9783030463823
Total Pages : 0 pages
Book Rating : 4.4/5 (638 download)

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Book Synopsis New Statistics for Design Researchers by : Martin Schmettow

Download or read book New Statistics for Design Researchers written by Martin Schmettow and published by Springer. This book was released on 2022-07-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design Research uses scientific methods to evaluate designs and build design theories. This book starts with recognizable questions in Design Research, such as A/B testing, how users learn to operate a device and why computer-generated faces are eerie. Using a broad range of examples, efficient research designs are presented together with statistical models and many visualizations. With the tidy R approach, producing publication-ready statistical reports is straight-forward and even non-programmers can learn this in just one day. Hundreds of illustrations, tables, simulations and models are presented with full R code and data included. Using Bayesian linear models, multi-level models and generalized linear models, an extensive statistical framework is introduced, covering a huge variety of research situations and yet, building on only a handful of basic concepts. Unique solutions to recurring problems are presented, such as psychometric multi-level models, beta regression for rating scales and ExGaussian regression for response times. A “think-first” approach is promoted for model building, as much as the quantitative interpretation of results, stimulating readers to think about data generating processes, as well as rational decision making. New Statistics for Design Researchers: A Bayesian Workflow in Tidy R targets scientists, industrial researchers and students in a range of disciplines, such as Human Factors, Applied Psychology, Communication Science, Industrial Design, Computer Science and Social Robotics. Statistical concepts are introduced in a problem-oriented way and with minimal formalism. Included primers on R and Bayesian statistics provide entry point for all backgrounds. A dedicated chapter on model criticism and comparison is a valuable addition for the seasoned scientist.

Mixed Effects Models for Complex Data

Author : Lang Wu
Publisher : CRC Press
ISBN 13 : 9781420074086
Total Pages : 431 pages
Book Rating : 4.0/5 (74 download)

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Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu

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.

Generalized Linear Models With Examples in R

Author : Peter K. Dunn
Publisher : Springer
ISBN 13 : 1441901183
Total Pages : 573 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Generalized Linear Models With Examples in R by : Peter K. Dunn

Download or read book Generalized Linear Models With Examples in R written by Peter K. Dunn and published by Springer. This book was released on 2018-11-10 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities. The book is designed with the student in mind, making it suitable for self-study or a structured course. Beginning with an introduction to linear regression, the book also devotes time to advanced topics not typically included in introductory textbooks. It features chapter introductions and summaries, clear examples, and many practice problems, all carefully designed to balance theory and practice. The text also provides a working knowledge of applied statistical practice through the extensive use of R, which is integrated into the text. Other features include: • Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals • Nearly 100 data sets in the companion R package GLMsData • Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session

Generalized Linear Models

Author : Raymond H. Myers
Publisher : John Wiley & Sons
ISBN 13 : 0470556978
Total Pages : 521 pages
Book Rating : 4.4/5 (75 download)

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Book Synopsis Generalized Linear Models by : Raymond H. Myers

Download or read book Generalized Linear Models written by Raymond H. Myers and published by John Wiley & Sons. This book was released on 2012-01-20 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities." —Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: A new chapter on random effects and designs for GLMs A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights Illustrations of R code to perform GLM analysis The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.

The Theory of Dispersion Models

Author : Bent Jorgensen
Publisher : CRC Press
ISBN 13 : 9780412997112
Total Pages : 264 pages
Book Rating : 4.9/5 (971 download)

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Book Synopsis The Theory of Dispersion Models by : Bent Jorgensen

Download or read book The Theory of Dispersion Models written by Bent Jorgensen and published by CRC Press. This book was released on 1997-06-01 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of dispersion models straddles both statistics and probability, and involves an encyclopedic collection of tools, such as exponential families, asymptotic theory, stochastic processes, Tauber theory, infinite divisibility, and stable distributions. The Theory of Dispersion Models introduces the reader to these models, which serve as error distributions for generalized linear models, and looks at their applications within this context.

Modern Regression Techniques Using R

Author : Daniel B Wright
Publisher : SAGE
ISBN 13 : 1446206025
Total Pages : 217 pages
Book Rating : 4.4/5 (462 download)

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Book Synopsis Modern Regression Techniques Using R by : Daniel B Wright

Download or read book Modern Regression Techniques Using R written by Daniel B Wright and published by SAGE. This book was released on 2009-02-19 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books′ webpage: www.sagepub.co.uk/wrightandlondon. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter. Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets. The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.

Generalized Additive Models

Author : Simon Wood
Publisher : CRC Press
ISBN 13 : 1584884746
Total Pages : 412 pages
Book Rating : 4.5/5 (848 download)

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Book Synopsis Generalized Additive Models by : Simon Wood

Download or read book Generalized Additive Models written by Simon Wood and published by CRC Press. This book was released on 2006-02-27 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.

Biostatistics for Animal Science, 3rd Edition

Author : Miroslav Kaps
Publisher : CABI
ISBN 13 : 1786390353
Total Pages : 563 pages
Book Rating : 4.7/5 (863 download)

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Book Synopsis Biostatistics for Animal Science, 3rd Edition by : Miroslav Kaps

Download or read book Biostatistics for Animal Science, 3rd Edition written by Miroslav Kaps and published by CABI. This book was released on 2017-06-23 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed to cover techniques for analysis of data in the animal sciences, this popular textbook provides an overview of the basic principles of statistics enabling the subsequent applications to be carried out with familiarity and understanding. Each chapter begins by introducing a problem with practical questions, followed by a brief theoretical background. Most topics are followed up with numerical examples to illustrate the methods described using data-sets from animal sciences and related fields. The same examples are then solved using the SAS software package. Written primarily for students and researchers in animal sciences, the text is also useful for those studying agricultural, biological, and veterinary sciences.

R and Data Mining

Author : Yanchang Zhao
Publisher : Academic Press
ISBN 13 : 012397271X
Total Pages : 251 pages
Book Rating : 4.1/5 (239 download)

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Book Synopsis R and Data Mining by : Yanchang Zhao

Download or read book R and Data Mining written by Yanchang Zhao and published by Academic Press. This book was released on 2012-12-31 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work