Categorical Data Analysis

ISBN-10: 0471360937
ISBN-13: 9780471360933
Edition: 2nd 2002 (Revised)
Authors: Alan Agresti
List price: $170.00
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Description: Agresti provides a comprehensive introduction to methods for categorical data analysis. He presents the most prominent methods, such as logistic regression modelling and clearly presents new methods for correlated multivariate categorical responses.

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Book details

List price: $170.00
Edition: 2nd
Copyright year: 2002
Publisher: John Wiley & Sons, Incorporated
Publication date: 7/22/2002
Binding: Hardcover
Pages: 734
Size: 6.50" wide x 9.75" long x 1.75" tall
Weight: 2.750
Language: English

Agresti provides a comprehensive introduction to methods for categorical data analysis. He presents the most prominent methods, such as logistic regression modelling and clearly presents new methods for correlated multivariate categorical responses.

Introduction: Distributions and Inference for Categorical Data
Categorical Response Data
Distributions for Categorical Data
Statistical Inference for Categorical Data
Statistical Inference for Binomial Parameters
Statistical Inference for Multinomial Parameters
Describing Contingency Tables
Probability Structure for Contingency Tables
Comparing Two Proportions
Partial Association in Stratified 2 X 2 Tables
Extensions for I X J Tables
Inference for Contingency Tables
Confidence Intervals for Association Parameters
Testing Independence in Two-Way Contingency Tables
Following-Up Chi-Squared Tests
Two-Way Tables with Ordered Classifications
Small-Sample Tests of Independence
Small-Sample Confidence Intervals for 2 x 2 Tables
Extensions for Multiway Tables and Nontabulated Responses
Introduction to Generalized Linear Models
Generalized Linear Model
Generalized Linear Models for Binary Data
Generalized Linear Models for Counts
Moments and Likelihood for Generalized Linear Models
Inference for Generalized Linear Models
Fitting Generalized Linear Models
Quasi-likelihood and Generalized Linear Models
Generalized Additive Models
Logistic Regression
Interpreting Parameters in Logistic Regression
Inference for Logistic Regression
Logit Models with Categorical Predictors
Multiple Logistic Regression
Fitting Logistic Regression Models
Building and Applying Logistic Regression Models
Strategies in Model Selection
Logistic Regression Diagnostics
Inference About Conditional Associations in 2 x 2 x K Tables
Using Models to Improve Inferential Power
Sample Size and Power Considerations
Probit and Complementary Log-Log Models
Conditional Logistic Regression and Exact Distributions
Logit Models for Multinomial Responses
Nominal Responses: Baseline-Category Logit Models
Ordinal Responses: Cumulative Logit Models
Ordinal Responses: Cumulative Link Models
Alternative Models for Ordinal Responses
Testing Conditional Independence in I x J x K Tables
Discrete-Choice Multinomial Logit Models
Loglinear Models for Contingency Tables
Loglinear Models for Two-Way Tables
Loglinear Models for Independence and Interaction in Three-Way Tables
Inference for Loglinear Models
Loglinear Models for Higher Dimensions
The Loglinear-Logit Model Connection
Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions
Loglinear Model Fitting: Iterative Methods and their Application
Building and Extending Loglinear/Logit Models
Association Graphs and Collapsibility
Model Selection and Comparison
Diagnostics for Checking Models
Modeling Ordinal Associations
Association Models
Association Models, Correlation Models, and Correspondence Analysis
Poisson Regression for Rates
Empty Cells and Sparseness in Modeling Contingency Tables
Models for Matched Pairs
Comparing Dependent Proportions
Conditional Logistic Regression for Binary Matched Pairs
Marginal Models for Square Contingency Tables
Symmetry, Quasi-symmetry, and Quasi-independence
Measuring Agreement Between Observers
Bradley-Terry Model for Paired Preferences
Marginal Models and Quasi-symmetry Models for Matched Sets
Analyzing Repeated Categorical Response Data
Comparing Marginal Distributions: Multiple Responses
Marginal Modeling: Maximum Likelihood Approach
Marginal Modeling: Generalized Estimating Equations Approach
Quasi-likelihood and Its GEE Multivariate Extension: Details
Markov Chains: Transitional Modeling
Random Effects: Generalized Linear Mixed Models for Categorical Responses
Random Effects Modeling of Clustered Categorical Data
Binary Responses: Logistic-Normal Model
Examples of Random Effects Models for Binary Data
Random Effects Models for Multinomial Data
Multivariate Random Effects Models for Binary Data
GLMM Fitting, Inference, and Prediction
Other Mixture Models for Categorical Data
Latent Class Models
Nonparametric Random Effects Models
Beta-Binomial Models
Negative Binomial Regression
Poisson Regression with Random Effects
Asymptotic Theory for Parametric Models
Delta Method
Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities
Asymptotic Distributions of Residuals and Goodness-of-Fit Statistics
Asymptotic Distributions for Logit/Loglinear Models
Alternative Estimation Theory for Parametric Models
Weighted Least Squares for Categorical Data
Bayesian Inference for Categorical Data
Other Methods of Estimation
Historical Tour of Categorical Data Analysis
Pearson-Yule Association Controversy
R. A. Fisher's Contributions
Logistic Regression
Multiway Contingency Tables and Loglinear Models
Recent (and Future?) Developments
Using Computer Software to Analyze Categorical Data
Software for Categorical Data Analysis
Examples of SAS Code by Chapter
Chi-Squared Distribution Values
Examples Index
Author Index
Subject Index

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