Introduction to General and Generalized Linear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to General and Generalized Linear Models write by Henrik Madsen. This book was released on 2010-11-09. Introduction to General and Generalized Linear Models available in PDF, EPUB and Kindle. 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
Introduction to General and Generalized Linear Models
Introduction to General and Generalized Linear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to General and Generalized Linear Models write by Henrik Madsen. This book was released on 2010-11-09. Introduction to General and Generalized Linear Models available in PDF, EPUB and Kindle. 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 examples show how the problems are solved with R. After describing the necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the corresponding/parallel results for the general linear models first, since they are easier to understand and often more well known. The authors then explore random effects and mixed effects in a Gaussian context. They also introduce non-Gaussian hierarchical models that are members of the exponential family of distributions. Each chapter contains examples and guidelines for solving the problems via R. Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. Ancillary materials are available at www.imm.dtu.dk/~hm/GLM
Introduction to General and Generalized Linear Models
Introduction to General and Generalized Linear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to General and Generalized Linear Models write by Henrik Madsen. This book was released on 2024-10-14. Introduction to General and Generalized Linear Models available in PDF, EPUB and Kindle. Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. The aut
Generalized Linear Models With Examples in R
Generalized Linear Models With Examples in R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Generalized Linear Models With Examples in R write by Peter K. Dunn. This book was released on 2018-11-10. Generalized Linear Models With Examples in R available in PDF, EPUB and Kindle. 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
Foundations of Linear and Generalized Linear Models
Foundations of Linear and Generalized Linear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Foundations of Linear and Generalized Linear Models write by Alan Agresti. This book was released on 2015-02-23. Foundations of Linear and Generalized Linear Models available in PDF, EPUB and Kindle. 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.
