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The Theory Of Exponential Dispersion Models And Analysis Of Deviance
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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.
Download or read book Monografias de matemática written by and published by . This book was released on 1969 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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)
Book Synopsis Fundamentals of Statistical Exponential Families by : Lawrence D. Brown
Download or read book Fundamentals of Statistical Exponential Families written by Lawrence D. Brown and published by IMS. This book was released on 1986 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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
Book Synopsis Encyclopedia of Statistical Sciences, Volume 3 by :
Download or read book Encyclopedia of Statistical Sciences, Volume 3 written by and published by John Wiley & Sons. This book was released on 2005-12-16 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: ENCYCLOPEDIA OF STATISTICAL SCIENCES
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.
Book Synopsis Statistical Inference by : Paul H. Garthwaite
Download or read book Statistical Inference written by Paul H. Garthwaite and published by OUP Oxford. This book was released on 2002 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical inference is the foundation on which much of statistical practice is built. The book covers the topic at a level suitable for students and professionals who need to understand these foundations.
Book Synopsis Generalized Linear Models with Random Effects by : Youngjo Lee
Download or read book Generalized Linear Models with Random Effects written by Youngjo Lee and published by CRC Press. This book was released on 2006-07-13 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors. Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.
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.
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.
Book Synopsis Latent Variable Analysis and Signal Separation by : Emmanuel Vincent
Download or read book Latent Variable Analysis and Signal Separation written by Emmanuel Vincent and published by Springer. This book was released on 2015-08-14 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. The 61 revised full papers presented – 29 accepted as oral presentations and 32 accepted as poster presentations – were carefully reviewed and selected from numerous submissions. Five special topics are addressed: tensor-based methods for blind signal separation; deep neural networks for supervised speech separation/enhancement; joined analysis of multiple datasets, data fusion, and related topics; advances in nonlinear blind source separation; sparse and low rank modeling for acoustic signal processing.
Book Synopsis Information and Exponential Families by : O. Barndorff-Nielsen
Download or read book Information and Exponential Families written by O. Barndorff-Nielsen and published by John Wiley & Sons. This book was released on 2014-05-07 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: First published by Wiley in 1978, this book is being re-issued with a new Preface by the author. The roots of the book lie in the writings of RA Fisher both as concerns results and the general stance to statistical science, and this stance was the determining factor in the author's selection of topics. His treatise brings together results on aspects of statistical information, notably concerning likelihood functions, plausibility functions, ancillarity, and sufficiency, and on exponential families of probability distributions.
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.
Book Synopsis Journal of the American Statistical Association by : American Statistical Association
Download or read book Journal of the American Statistical Association written by American Statistical Association and published by . This book was released on 2003 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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
Book Synopsis Introduction to General and Generalized Linear Models by : Henrik Madsen
Download or read book Introduction to General and Generalized Linear Models written by Henrik Madsen and published by CRC Press. This book was released on 2010-11-09 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous
