Categorical Data Analysis 3rd edition by Alan Agresti – Ebook PDF Instant Download/Delivery: 1118467756 , 9781118467756
Full download Categorical Data Analysis 3rd edition after payment
Product details:
ISBN 10: 1118467756
ISBN 13: 9781118467756
Author: Alan Agresti
A must-have book for anyone expecting to do research and/or applications in categorical data analysis. Statistics in Medicine This classic book summarizes the latest and best methods for univariate and correlated multivariate categorical responses than any rival of its kind on the market today.
Categorical Data Analysis 3rd Table of contents:
1 Introduction: Distributions and Inference for Categorical Data
1.1 Categorical Response Data
1.2 Distributions for Categorical Data
1.3 Statistical Inference for Categorical Data
1.4 Statistical Inference for Binomial Parameters
1.5 Statistical Inference for Multinomial Parameters
1.6 Bayesian Inference for Binomial and Multinomial Parameters
Notes
Exercises
2 Describing Contingency Tables
2.1 Probability Structure for Contingency Tables
2.2 Comparing Two Proportions
2.3 Conditional Association in Stratified 2 × 2 Tables
2.4 Measuring Association in I × J Tables
Notes
Exercises
3 Inference for Two-Way Contingency Tables
3.1 Confidence Intervals for Association Parameters
3.2 Testing Independence in Two-way Contingency Tables
3.3 Following-up Chi-Squared Tests
3.4 Two-Way Tables with Ordered Classifications
3.5 Small-Sample Inference for Contingency Tables
3.6 Bayesian Inference for Two-way Contingency Tables
3.7 Extensions for Multiway Tables and Nontabulated Responses
Notes
Exercises
4 Introduction to Generalized Linear Models
4.1 The Generalized Linear Model
4.2 Generalized Linear Models for Binary Data
4.3 Generalized Linear Models for Counts and Rates
4.4 Moments and Likelihood for Generalized Linear Models
4.5 Inference and Model Checking for Generalized Linear Models
4.6 Fitting Generalized Linear Models
4.7 Quasi-Likelihood and Generalized Linear Models
Notes
Exercises
5 Logistic Regression
5.1 Interpreting Parameters in Logistic Regression
5.2 Inference for Logistic Regression
5.3 Logistic Models with Categorical Predictors
5.4 Multiple Logistic Regression
5.5 Fitting Logistic Regression Models
Notes
Exercises
6 Building, Checking, and Applying Logistic Regression Models
6.1 Strategies in Model Selection
6.2 Logistic Regression Diagnostics
6.3 Summarizing the Predictive Power of a Model
6.4 Mantel–Haenszel and Related Methods for Multiple 2 × 2 Tables
6.5 Detecting and Dealing with Infinite Estimates
6.6 Sample Size and Power Considerations
Notes
Exercises
7 Alternative Modeling of Binary Response Data
7.1 Probit and Complementary Log–log Models
7.2 Bayesian Inference for Binary Regression
7.3 Conditional Logistic Regression
7.4 Smoothing: Kernels Penalized Likelihood Generalized Additive Models
7.5 Issues in Analyzing High-Dimensional Categorical Data
Notes
Exercises
8 Models for Multinomial Responses
8.1 Nominal Responses: Baseline-Category Logit Models
8.2 Ordinal Responses: Cumulative Logit Models
8.3 Ordinal Responses: Alternative Models
8.4 Testing Conditional Independence in I × J × K Tables
8.5 Discrete-Choice Models
8.6 Bayesian Modeling of Multinomial Responses
Notes
Exercises
9 Loglinear Models for Contingency Tables
9.1 Loglinear Models for Two-way Tables
9.2 Loglinear Models for Independence and Interaction in Three-way Tables
9.3 Inference for Loglinear Models
9.4 Loglinear Models for Higher Dimensions
9.5 Loglinear—Logistic Model Connection
9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions
9.7 Loglinear Model Fitting: Iterative Methods and Their Application
Notes
Exercises
10 Building and Extending Loglinear Models
10.1 Conditional Independence Graphs and Collapsibility
10.2 Model Selection and Comparison
10.3 Residuals for Detecting Cell-Specific Lack of Fit
10.4 Modeling Ordinal Associations
10.5 Generalized Loglinear and Association Models Correlation Models and Correspondence Analysis
10.6 Empty Cells and Sparseness in Modeling Contingency Tables
10.7 Bayesian Loglinear Modeling
Notes
Exercises
11 Models for Matched Pairs
11.1 Comparing Dependent Proportions
11.2 Conditional Logistic Regression for Binary Matched Pairs
11.3 Marginal Models for Square Contingency Tables
11.4 Symmetry, Quasi-Symmetry, and Quasi-Independence
11.5 Measuring Agreement Between Observers
11.6 Bradley–Terry Model for Paired Preferences
11.7 Marginal Models and Quasi-Symmetry Models for Matched Sets
Notes
Exercises
12 Clustered Categorical Data: Marginal and Transitional Models
12.1 Marginal Modeling: Maximum Likelihood Approach
12.2 Marginal Modeling: Generalized Estimating Equations (GEEs) Approach
12.3 Quasi-Likelihood and Its GEE Multivariate Extension: Details
12.4 Transitional Models: Markov Chain and Time Series Models
Notes
Exercises
13 Clustered Categorical Data: Random Effects Models
13.1 Random Effects Modeling of Clustered Categorical Data
13.2 Binary Responses: Logistic-Normal Model
13.3 Examples of Random Effects Models for Binary Data
13.4 Random Effects Models for Multinomial Data
13.5 Multilevel Modeling
13.6 GLMM Fitting Inference and Prediction
13.7 Bayesian Multivariate Categorical Modeling
Notes
Exercises
14 Other Mixture Models for Discrete Data
14.1 Latent Class Models
14.2 Nonparametric Random Effects Models
14.3 Beta-Binomial Models
14.4 Negative Binomial Regression
14.5 Poisson Regression with Random Effects
Notes
Exercises
15 Non-Model-Based Classification and Clustering
15.1 Classification: Linear Discriminant Analysis
15.2 Classification: Tree-Structured Prediction
15.3 Cluster Analysis for Categorical Data
Notes
Exercises
16 Large- and Small-Sample Theory for Multinomial Models
16.1 Delta Method
16.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities
16.3 Asymptotic Distributions of Residuals and Goodness-of-fit Statistics
16.4 Asymptotic Distributions for Logit/Loglinear Models
16.5 Small-Sample Significance Tests for Contingency Tables
16.6 Small-Sample Confidence Intervals for Categorical Data
16.7 Alternative Estimation Theory for Parametric Models
Notes
Exercises
17 Historical Tour of Categorical Data Analysis
17.1 Pearson–Yule Association Controversy
17.2 R. A. Fisher’s Contributions
17.3 Logistic Regression
17.4 Multiway Contingency Tables and Loglinear Models
17.5 Bayesian Methods for Categorical Data
17.6 A Look Forward, and Backward
Appendix A Statistical Software for Categorical Data Analysis
Appendix B Chi-Squared Distribution Values
References
Author Index
Example Index
Subject Index
People also search for Categorical Data Analysis 3rd:
an introduction to categorical data analysis
agresti categorical data analysis
an introduction to categorical data analysis 3rd edition pdf
statistical methods for categorical data analysis
an introduction to categorical data analysis 3rd edition
Tags: Alan Agresti, Categorical Data