An Introduction to Generalized Linear Models

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Juni 2008



Popular for its accessible, concise, and clear introduction to this key statistical methodology, An Introduction to Generalized Linear Models, Third Edition provides a wealth of examples from such diverse fields as business, medicine, engineering, and the social sciences. Emphasizing graphical methods for exploratory data analysis and visualization, this new edition offers more material on Bayesian methodology and additional advice on implementing methods using statistical software. It also has updated the examples and exercises and includes an appendix of selected solutions, enhancing its suitability for self-study.


Introduction Background Scope Notation Distributions Related to the Normal Distribution Quadratic Forms Estimation Model Fitting Introduction Examples Some Principles of Statistical Modeling Notation and Coding for Explanatory Variables Exponential Family and Generalized Linear Models Introduction Exponential Family of Distributions Properties of Distributions in the Exponential Family Generalized Linear Models Examples Estimation Introduction Example: Failure Times for Pressure Vessels Maximum Likelihood Estimation Poisson Regression Example Inference Introduction Sampling Distribution for Score Statistics Taylor Series Approximations Sampling Distribution for MLEs Log-Likelihood Ratio Statistic Sampling Distribution for the Deviance Hypothesis Testing Normal Linear Models Introduction Basic Results Multiple Linear Regression Analysis of Variance Analysis of Covariance General Linear Models Binary Variables and Logistic Regression Probability Distributions Generalized Linear Models Dose Response Models General Logistic Regression Model Goodness-of-Fit Statistics Residuals Other Diagnostics Example: Senility and WAIS Nominal and Ordinal Logistic Regression Introduction Multinomial Distribution Nominal Logistic Regression Ordinal Logistic Regression General Comments Poisson Regression and Log-Linear Models Introduction Poisson Regression Examples of Contingency Tables Probability Models for Contingency Tables Log-Linear Models Inference for Log-Linear Models Numerical Examples Remarks Survival Analysis Introduction Survivor Functions and Hazard Functions Empirical Survivor Function Estimation Inference Model Checking Example: Remission Times Clustered and Longitudinal Data Introduction Example: Recovery from Stroke Repeated Measures Models for Normal Data Repeated Measures Models for Non-Normal Data Multilevel Models Stroke Example Continued Comments Bayesian Analysis Frequentist and Bayesian Paradigms Priors Distributions and Hierarchies in Bayesian Analysis WinBUGS Software for Bayesian Analysis Methods Why Standard Inference Fails Monte Carlo Integration Markov Chains Bayesian Inference Diagnostics of Chain Convergence Bayesian Model Fit: The DIC Example Bayesian Analyses Introduction Binary Variables and Logistic Regression Nominal Logistic Regression Latent Variable Model Survival Analysis Random Effects Longitudinal Data Analysis Some Practical Tips for WinBUGS Software References Index Exercises appear at the end of each chapter.


Overall, this new edition remains a highly useful and compact introduction to a large number of seemingly disparate regression models. Depending on the background of the audience, it will be suitable for upper-level undergraduate or beginning post-graduate courses. -Christian Kleiber, Statistical Papers (2012) 53The comments of Lang in his review of the second edition, that 'This relatively short book gives a nice introductory overview of the theory underlying generalized linear modelling. ...' can equally be applied to the new edition. ... three new chapters on Bayesian analysis are also added. ... suitable for experienced professionals needing to refresh their knowledge ... . -Pharmaceutical Statistics, 2011 The chapters are short and concise, and the writing is clear ... explanations are fundamentally sound and aimed well at an upper-level undergrad or early graduate student in a statistics-related field. This is a very worthwhile book: a good class text and a practical reference for applied statisticians. -Biometrics This book promises in its introductory section to provide a unifying framework for many statistical techniques. It accomplishes this goal easily. ... Furthermore, the text covers important topics that are frequently overlooked in introductory courses, such as models for ordinal outcomes. ... This book is an excellent resource, either as an introduction to or a reminder of the technical aspects of generalized linear models and provides a wealth of simple yet useful examples and data sets. -Journal of Biopharmaceutical Statistics, Issue 2 Praise for the Second Edition: The second edition ... is successful in filling a void in the otherwise sparse literature on the subject of generalized linear models at the introductory level ... a wide range of research applications are covered and ample workings are also provided to aid the reader in statistical calculations ... I would highly recommend this text ... . -Kerrie Nelson, Statistics in Medicine, Vol. 23
EAN: 9781584889502
ISBN: 1584889500
Untertitel: 'Chapman & Hall/CRC Texts in Statistical Science'. 3 Rev ed. 59 black & white illustrations, 101 black & white tables. Sprache: Englisch.
Verlag: Taylor & Francis Inc
Erscheinungsdatum: Juni 2008
Seitenanzahl: 320 Seiten
Format: gebunden
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