Introduction to the Statistical Analysis of Categorical Data / Edition 1

by Erling B. Andersen

Overview

This book deals with the analysis of categorical data. Statistical models, especially log-linear models for contingency tables and logistic regression, are described and applied to real life data. Special emphasis is given to the use of graphical methods. The book is intended as a text for both undergraduate and graduate courses for statisticians, applied statisticians, social scientists, economists and epidemiologists. Many examples and exercises with solutions should help the reader to understand the material.

Product Details

ISBN-13:	9783540623991
Publisher:	Springer Berlin Heidelberg
Publication date:	10/25/2001
Edition description:	Softcover reprint of the original 1st ed. 1997
Pages:	265
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

1 Introduction.- 1.1 The two-way table.- 2 Basic Theory.- 2.1 Introduction.- 2.2 Exponential families.- 2.3 Statistical inference in an exponential family.- 2.4 The binomial distribution.- 2.5 The multinomial distribution.- 2.6 The Poisson distribution.- 2.7 Composite hypotheses.- 2.8 Applications to the multinomial distribution.- 2.9 Log-linear models.- 2.10 The two-way contingency table.- 2.11 The numerical solution of the likelihood equations for the log-linear model.- 2.12 Bibliographical notes.- 2.13 Exercises.- 3 Three-way contingency tables.- 3.1 Log-linear models.- 3.2 Log-linear hypotheses.- 3.3 Estimation.- 3.4 Testing hypotheses.- 3.5 Interpretation of the log-linear parameters.- 3.6 Choice of model.- 3.7 Detection of model deviations.- 3.8 Bibliographical notes.- 3.9 Exercises.- 4 Multi-dimensional contingency tables.- 4.1 The log-linear model.- 4.2 Classification and interpretation of log-linear models.- 4.3 Choice of model.- 4.4 Diagnostics.- 4.5 Model search strategies.- 4.6 Bibliographical notes.- 4.7 Exercises.- 5 Incomplete Tables.- 5.1 Random and structural zeros.- 5.2 Counting the number of degrees of freedom.- 5.3 Validity of the X2-approximation.- 5.4 Exercises.- 6 The Logit Model.- 6.1 The logit model.- 6.2 Hypothesis testing in the logit model.- 6.3 Logit models with higher order interactions.- 6.4 The logit model as a regression model.- 6.5 Bibliographical notes.- 6.6 Exercises.- 7 Logistic Regression Analysis.- 7.1 The logistic regression model.- 7.2 Estimation in the logistic regression model.- 7.3 Numerical solution of the likelihood equations.- 7.4 Checking the fit of the model.- 7.5 Hypothesis testing.- 7.6 Diagnostics.- 7.7 Predictions.- 7.8 Dummy variables.- 7.9 Polytomous response variables.- 7.10 Bibliographical notes.- 7.11 Exercises.-8 Association Models.- 8.1 Introduction.- 8.2 Symmetry models.- 8.3 Marginal homogeneity.- 8.4 RC-association models.- 8.5 Correspondence analysis.- 8.6 Bibliographical notes.- 8.5 Exercises.- Appendix Solutions and output to selected exercises.- References.