Generalized Linear Models And Correlated Data Methods

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Beyond Multiple Linear Regression

Author : Paul Roback
Publisher : CRC Press
ISBN 13 : 1439885400
Total Pages : 436 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Beyond Multiple Linear Regression by : Paul Roback

Download or read book Beyond Multiple Linear Regression written by Paul Roback and published by CRC Press. This book was released on 2021-01-14 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)

Generalized Linear Models and Correlated Data Methods

Author : Julie Legler
Publisher : Chapman and Hall/CRC
ISBN 13 : 9781439885383
Total Pages : 400 pages
Book Rating : 4.8/5 (853 download)

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Book Synopsis Generalized Linear Models and Correlated Data Methods by : Julie Legler

Download or read book Generalized Linear Models and Correlated Data Methods written by Julie Legler and published by Chapman and Hall/CRC. This book was released on 2020-09-15 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed for advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II as well as courses on Generalized Linear Models, Longitudinal Data Analysis, Correlated Data, or Multilevel Models, this text offers a unified discussion of generalized linear models and correlated data methods. It explores case studies involving real data and details material on R at the end of each chapter. A solutions manual is available for qualified instructors.

Generalized Linear Models for Insurance Data

Author : Piet de Jong
Publisher : Cambridge University Press
ISBN 13 : 1139470477
Total Pages : 207 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Generalized Linear Models for Insurance Data by : Piet de Jong

Download or read book Generalized Linear Models for Insurance Data written by Piet de Jong and published by Cambridge University Press. This book was released on 2008-02-28 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.

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.

Generalized Linear Models

Author : John Patrick Hoffmann
Publisher : Addison-Wesley Longman
ISBN 13 :
Total Pages : 220 pages
Book Rating : 4.:/5 (321 download)

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Book Synopsis Generalized Linear Models by : John Patrick Hoffmann

Download or read book Generalized Linear Models written by John Patrick Hoffmann and published by Addison-Wesley Longman. This book was released on 2004 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing, and business. Less theoretical than competing texts, Hoffman includes numerous exercises and worked-out examples and sample programs and data sets for three popular statistical software programs: SPSS, SAS, and Stata.

Generalized Linear Mixed Models

Author : Walter W. Stroup
Publisher : CRC Press
ISBN 13 : 1439815135
Total Pages : 547 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Generalized Linear Mixed Models by : Walter W. Stroup

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.

An Introduction to Generalized Linear Models

Author : Annette J. Dobson
Publisher : CRC Press
ISBN 13 : 1584889519
Total Pages : 316 pages
Book Rating : 4.5/5 (848 download)

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Book Synopsis An Introduction to Generalized Linear Models by : Annette J. Dobson

Download or read book An Introduction to Generalized Linear Models written by Annette J. Dobson and published by CRC Press. This book was released on 2008-05-12 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.

Modelling Longitudinal and Spatially Correlated Data

Author : Timothy G. Gregoire
Publisher : Springer Science & Business Media
ISBN 13 : 1461206995
Total Pages : 404 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Modelling Longitudinal and Spatially Correlated Data by : Timothy G. Gregoire

Download or read book Modelling Longitudinal and Spatially Correlated Data written by Timothy G. Gregoire and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Correlated data arise in numerous contexts across a wide spectrum of subject-matter disciplines. Modeling such data present special challenges and opportunities that have received increasing scrutiny by the statistical community in recent years. In October 1996 a group of 210 statisticians and other scientists assembled on the small island of Nantucket, U. S. A. , to present and discuss new developments relating to Modelling Longitudinal and Spatially Correlated Data: Methods, Applications, and Future Direc tions. Its purpose was to provide a cross-disciplinary forum to explore the commonalities and meaningful differences in the source and treatment of such data. This volume is a compilation of some of the important invited and volunteered presentations made during that conference. The three days and evenings of oral and displayed presentations were arranged into six broad thematic areas. The session themes, the invited speakers and the topics they addressed were as follows: • Generalized Linear Models: Peter McCullagh-"Residual Likelihood in Linear and Generalized Linear Models" • Longitudinal Data Analysis: Nan Laird-"Using the General Linear Mixed Model to Analyze Unbalanced Repeated Measures and Longi tudinal Data" • Spatio---Temporal Processes: David R. Brillinger-"Statistical Analy sis of the Tracks of Moving Particles" • Spatial Data Analysis: Noel A. Cressie-"Statistical Models for Lat tice Data" • Modelling Messy Data: Raymond J. Carroll-"Some Results on Gen eralized Linear Mixed Models with Measurement Error in Covariates" • Future Directions: Peter J.

Applied Regression Analysis and Generalized Linear Models

Author : John Fox
Publisher : SAGE Publications
ISBN 13 : 1483321312
Total Pages : 612 pages
Book Rating : 4.4/5 (833 download)

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Book Synopsis Applied Regression Analysis and Generalized Linear Models by : John Fox

Download or read book Applied Regression Analysis and Generalized Linear Models written by John Fox and published by SAGE Publications. This book was released on 2015-03-18 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website.

Generalized Linear and Nonlinear Models for Correlated Data

Author : Edward F. Vonesh
Publisher : SAS Institute
ISBN 13 : 1629592307
Total Pages : 529 pages
Book Rating : 4.6/5 (295 download)

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Book Synopsis Generalized Linear and Nonlinear Models for Correlated Data by : Edward F. Vonesh

Download or read book Generalized Linear and Nonlinear Models for Correlated Data written by Edward F. Vonesh and published by SAS Institute. This book was released on 2014-07-07 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: Edward Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately. This book is part of the SAS Press program.

Generalized Linear Models

Author : P. McCullagh
Publisher : Routledge
ISBN 13 : 1351445847
Total Pages : 361 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 361 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 Mixed Models

Author : Charles E. McCulloch
Publisher : IMS
ISBN 13 : 9780940600546
Total Pages : 100 pages
Book Rating : 4.6/5 (5 download)

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Book Synopsis Generalized Linear Mixed Models by : Charles E. McCulloch

Download or read book Generalized Linear Mixed Models written by Charles E. McCulloch and published by IMS. This book was released on 2003 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wiley Series in Probability and Statistics A modern perspective on mixed models The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features: * A review of the basics of linear models and linear mixed models * Descriptions of models for nonnormal data, including generalized linear and nonlinear models * Analysis and illustration of techniques for a variety of real data sets * Information on the accommodation of longitudinal data using these models * Coverage of the prediction of realized values of random effects * A discussion of the impact of computing issues on mixed models

Regression, ANOVA, and the General Linear Model

Author : Peter Vik
Publisher : SAGE Publications
ISBN 13 : 1483310337
Total Pages : 345 pages
Book Rating : 4.4/5 (833 download)

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Book Synopsis Regression, ANOVA, and the General Linear Model by : Peter Vik

Download or read book Regression, ANOVA, and the General Linear Model written by Peter Vik and published by SAGE Publications. This book was released on 2013-01-14 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Peter Vik's Regression, ANOVA, and the General Linear Model: A Statistics Primer demonstrates basic statistical concepts from two different perspectives, giving the reader a conceptual understanding of how to interpret statistics and their use. The two perspectives are (1) a traditional focus on the t-test, correlation, and ANOVA, and (2) a model-comparison approach using General Linear Models (GLM). This book juxtaposes the two approaches by presenting a traditional approach in one chapter, followed by the same analysis demonstrated using GLM. By so doing, students will acquire a theoretical and conceptual appreciation for data analysis as well as an applied practical understanding as to how these two approaches are alike.

Correlated Data Analysis: Modeling, Analytics, and Applications

Author : Peter X. -K. Song
Publisher : Springer Science & Business Media
ISBN 13 : 038771393X
Total Pages : 352 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Correlated Data Analysis: Modeling, Analytics, and Applications by : Peter X. -K. Song

Download or read book Correlated Data Analysis: Modeling, Analytics, and Applications written by Peter X. -K. Song and published by Springer Science & Business Media. This book was released on 2007-06-30 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas.

Generalized Linear Models

Author : Dipak K. Dey
Publisher : CRC Press
ISBN 13 : 9780824790349
Total Pages : 450 pages
Book Rating : 4.7/5 (93 download)

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Book Synopsis Generalized Linear Models by : Dipak K. Dey

Download or read book Generalized Linear Models written by Dipak K. Dey and published by CRC Press. This book was released on 2000-05-25 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.

An Introduction to Generalized Linear Models

Author : Annette J. Dobson
Publisher : CRC Press
ISBN 13 : 1351726226
Total Pages : 376 pages
Book Rating : 4.3/5 (517 download)

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Book Synopsis An Introduction to Generalized Linear Models by : Annette J. Dobson

Download or read book An Introduction to Generalized Linear Models written by Annette J. Dobson and published by CRC Press. This book was released on 2018-04-17 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC methods to fit GLMs Contains numerous examples from business, medicine, engineering, and the social sciences Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods Offers the data sets and solutions to the exercises online Describes the components of good statistical practice to improve scientific validity and reproducibility of results. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.

Generalized Linear Models

Author : Jeff Gill
Publisher : SAGE Publications
ISBN 13 : 1506320244
Total Pages : 97 pages
Book Rating : 4.5/5 (63 download)

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Book Synopsis Generalized Linear Models by : Jeff Gill

Download or read book Generalized Linear Models written by Jeff Gill and published by SAGE Publications. This book was released on 2000-08-07 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author explains the theoretical underpinnings of generalized linear models so that researchers can decide how to select the best way to adapt their data for this type of analysis. Examples are provided to illustrate the application of GLM to actual data and the author includes his Web address where additional resources can be found.