St. Thomas College Library

Categorical data analysis / (Record no. 86953)

000 -LEADER
fixed length control field 07802cam a22003014a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250718141904.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 120417s2013 njua b 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780470463635
040 ## - CATALOGING SOURCE
Transcribing agency STCPL
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.535
Item number ALA-C
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Agresti, Alan
245 10 - TITLE STATEMENT
Title Categorical data analysis /
Statement of responsibility, etc. by Alan Agresti
250 ## - EDITION STATEMENT
Edition statement 3rd ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Hoboken, NJ :
Name of publisher, distributor, etc. Wiley,
Date of publication, distribution, etc. c2013.
300 ## - PHYSICAL DESCRIPTION
Extent xvi, 714 p.
Other physical details Hard Back
490 0# - SERIES STATEMENT
Series statement Wiley series in probability and statistics ;
Volume/sequential designation 792
520 ## - SUMMARY, ETC.
Summary, etc. "A classic in its own right, this book continues to provide an introduction to modern generalized linear models for categorical variables. The text emphasizes methods that are most commonly used in practical application, such as classical inferences for two- and three-way contingency tables, logistic regression, loglinear models, models for multinomial (nominal and ordinal) responses, and methods for repeated measurement and other forms of clustered, correlated response data. Chapter headings remain essentially with the exception of a new one on Bayesian inference for parametric models. Other major changes include an expansion of clustered data, new research on analysis of data sets with robust variables, extensive discussions of ordinal data, more on interpretation, and additional exercises throughout the book. R and SAS are now showcased as the software of choice. An author web site with solutions, commentaries, software programs, and data sets is available"--
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Statistics
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Probability & Statistics
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Multivariate analysis
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data analysis
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note Machine generated contents note: Preface 1. Introduction: Distributions and Inference for Categorical Data 1 1.1 Categorical Response Data, 1 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 2x2 Tables 2.4 Measuring Association in I x 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 6.3 Mantel-Haenszel and Related Methods for Multiple 2x2 Tables 6.4 Detecting and Dealing with Infinite Estimates 6.5 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 The 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 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: The Logistic-Normal Model 13.3 Examples of Random Effects Models for Binary Data 13.4 Random Effects Models for Multinomial Data 13.5 Multilevel Models 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 15.2 Classification: Linear Discriminant Analysis 15.3 Classification: Tree-Structured Prediction 15.4 Cluster Analysis for Categorical Data Notes Exercises 16. Large- and Small-Sample Theory for Parametric 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.
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    Dewey Decimal Classification   Statistics Reference Book ST. THOMAS COLLEGE LIBRARY, PALAI ST. THOMAS COLLEGE LIBRARY, PALAI Statistics - Reference 2025-07-18 14680.55   519.535 ALA-C 92795 2025-07-18 2025-07-18 Books
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