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9780367173876

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This book fills an important gap in the field of categorical data analysis by combining a rigorous theoretical treatment of the subject matter with hands-on techniques to get the reader started in state-of-the-art statistical modeling. The topics covered in this book cannot easily be separated from parallel developments in computing, including modern software components that exploit advances in computing machinery. This is an excellent reference book, benefitting applied researchers wishing to understand and use advanced methodologies and explore the relevance of Bayesian approaches as well as machine learning. It also serves well as an advanced graduate textbook for graduate courses in categorical data analysis with a focus on R and modern Bayesian implementations available in Stan. - Dan Powers, University of Texas at Austin

This book fills an important gap in the field of categorical data analysis by combining a rigorous theoretical treatment of the subject matter with hands-on techniques to get the reader started in state-of-the-art statistical modeling. The topics covered in this book cannot easily be separated from parallel developments in computing, including modern software components that exploit advances in computing machinery. This is an excellent reference book, benefitting applied researchers wishing to understand and use advanced methodologies and explore the relevance of Bayesian approaches as well as machine learning. It also serves well as an advanced graduate textbook for graduate courses in categorical data analysis with a focus on R and modern Bayesian implementations available in Stan. - Dan Powers, University of Texas, Texas

This book fills an important gap in the field of categorical data analysis by combining a rigorous theoretical treatment of the subject matter with hands-on techniques to get the reader started in state-of-the-art statistical modeling. The topics covered in this book cannot easily be separated from parallel developments in computing, including modern software components that exploit advances in computing machinery. This is an excellent reference book, benefitting applied researchers wishing to understand and use advanced methodologies and explore the relevance of Bayesian approaches as well as machine learning. It also serves well as an advanced graduate textbook for graduate courses in categorical data analysis with a focus on R and modern Bayesian implementations available in Stan. - Dan Powers, University of Texas at Austin There are many outstanding books that show how to use Stata for Categorical Data Analysis. I am pleased that R users finally have a book that competes with the best of them; and given his outstanding record, I am not surprised that Jun Xu is the person who has written that book. For those with a basic background in statistical methods, Modern Applied Regressions provides a solid explanation of advanced methods like logistic regression, ordinal and multinomial models, count models, and survival analysis, using both Bayesian and Frequentist approaches. If there were no statistical code in it, the book would still be excellent because of the straightforward ways it explains methods. Certainly, there are a lot of equations, but those are coupled with intuitive explanations and examples. But, the use of R and Stan is what makes the book a real standout for me. For those who learn best by doing (and I count myself among them) the numerous examples of statistical code and output are invaluable. I'll enthusiastically recommend this book to anyone who is interested in its topics. - Richard Williams, Univeristy of Notre Dame

Author Information

Dr. Jun Xu is professor of sociology and data science at Ball State University. His quantitative research interests include Bayesian statistics, categorical data analysis, causal inference, machine learning, and statistical programming. His methodological works have appeared in journals such as Sociological Methods and Research, Social Science Research, and The Stata Journal. He is an author of Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives (with Dr. Andrew S. Fullerton by Chapman & Hall). In the past two decades or so, he has authored or co-authored several statistical application commands and packages, including gencrm, grcompare and the popular SPost9.0 package in Stata, and stdcoef in R.

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