Generalized Linear Models for Categorical and Continuous Limited Dependent Variables

Generalized Linear Models for Categorical and Continuous Limited Dependent Variables
Title Generalized Linear Models for Categorical and Continuous Limited Dependent Variables PDF eBook
Author Michael Smithson
Publisher CRC Press
Pages 310
Release 2013-09-05
Genre Mathematics
ISBN 1466551739

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Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages. The book provides broad, but unified, coverage, and the authors integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables. The authors argue that these dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. They cover special cases or extensions of models, estimation methods, model diagnostics, and, of course, software. They also discuss bounded continuous variables, boundary-inflated models, and methods for modeling heteroscedasticity. Wherever possible, the authors have illustrated concepts, models, and techniques with real or realistic datasets and demonstrations in R and Stata, and each chapter includes several exercises at the end. The illustrations and exercises help readers build conceptual understanding and fluency in using these techniques. At several points the authors bring together material that has been previously scattered across the literature in journal articles, software package documentation files, and blogs. These features help students learn to choose the appropriate models for their purpose.

Regression Models for Categorical and Limited Dependent Variables

Regression Models for Categorical and Limited Dependent Variables
Title Regression Models for Categorical and Limited Dependent Variables PDF eBook
Author J. Scott Long
Publisher SAGE
Pages 334
Release 1997-01-09
Genre Mathematics
ISBN 9780803973749

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Evaluates the most useful models for categorical and limited dependent variables (CLDVs), emphasizing the links among models and applying common methods of derivation, interpretation, and testing. The author also explains how models relate to linear regression models whenever possible. Annotation c.

Generalized Linear Models for Categorical and Continuous Limited Dependent Variables

Generalized Linear Models for Categorical and Continuous Limited Dependent Variables
Title Generalized Linear Models for Categorical and Continuous Limited Dependent Variables PDF eBook
Author Michael Smithson
Publisher CRC Press
Pages 300
Release 2013-09-05
Genre Mathematics
ISBN 1466551755

Download Generalized Linear Models for Categorical and Continuous Limited Dependent Variables Book in PDF, Epub and Kindle

Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages.The book provides br

Regression Models for Categorical Dependent Variables Using Stata, Second Edition

Regression Models for Categorical Dependent Variables Using Stata, Second Edition
Title Regression Models for Categorical Dependent Variables Using Stata, Second Edition PDF eBook
Author J. Scott Long
Publisher Stata Press
Pages 559
Release 2006
Genre Computers
ISBN 1597180114

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The goal of the book is to make easier to carry out the computations necessary for the full interpretation of regression nonlinear models for categorical outcomes usign Stata.

Foundations of Linear and Generalized Linear Models

Foundations of Linear and Generalized Linear Models
Title Foundations of Linear and Generalized Linear Models PDF eBook
Author Alan Agresti
Publisher John Wiley & Sons
Pages 471
Release 2015-02-23
Genre Mathematics
ISBN 1118730038

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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.

Regression & Linear Modeling

Regression & Linear Modeling
Title Regression & Linear Modeling PDF eBook
Author Jason W. Osborne
Publisher SAGE Publications
Pages 489
Release 2016-03-24
Genre Psychology
ISBN 1506302750

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In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.

Applying Generalized Linear Models

Applying Generalized Linear Models
Title Applying Generalized Linear Models PDF eBook
Author James K. Lindsey
Publisher Springer Science & Business Media
Pages 265
Release 2008-01-15
Genre Mathematics
ISBN 038722730X

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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.