Categorical Data Analysis, Second Edition

✍ Scribed by Alan Agresti(auth.)

• Publisher: John Wiley & Sons, Inc.
• Year: 2002
• Tongue: English
• Leaves: 729
• Series: Wiley Series in Probability and Statistics
• Category: Library

✦ Synopsis

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Categorical Data Analysis was among those chosen.

A valuable new edition of a standard reference

"A 'must-have' book for anyone expecting to do research and/or applications in categorical data analysis."
-Statistics in Medicine on Categorical Data Analysis, First Edition

The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis.

Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of:

• Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects Content:
  Chapter 1 Introduction: Distributions and Inference for Categorical Data (pages 1–35):
  Chapter 2 Describing Contingency Tables (pages 36–69):
  Chapter 3 Inference for Contingency Tables (pages 70–114):
  Chapter 4 Introduction to Generalized Linear Models (pages 115–164):
  Chapter 5 Logistic Regression (pages 165–210):
  Chapter 6 Building and Applying Logistic Regression Models (pages 211–266):
  Chapter 7 Logit Models for Multinomial Responses (pages 267–313):
  Chapter 8 Loglinear Models for Contingency Tables (pages 314–356):
  Chapter 9 Building and Extending Loglinear/Logit Models (pages 357–408):
  Chapter 10 Models for Matched Pairs (pages 409–454):
  Chapter 11 Analyzing Repeated Categorical Response Data (pages 455–490):
  Chapter 12 Random Effects: Generalized Linear Mixed Models for Categorical Responses (pages 491–537):
  Chapter 13 Other Mixture Models for Categorical Data (pages 538–575):
  Chapter 14 Asymptotic Theory for Parametric Models (pages 576–599):
  Chapter 15 Alternative Estimation Theory for Parametric Models (pages 600–618):
  Chapter 16 Historical Tour of Categorical Data Analysis (pages 619–631):