1. Introduction 2. Binary Regression 3. Polytomous Regression 4. Count Regression 5. Survival Regression 6. Extensions
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.
"I think that the text is a very brave attempt at bringing together
(most of) the major topics in the analysis of CLRVs (we will return
to this), from both Frequentist and Bayesian approaches, along with
a very hands-on and empirical approach, and all bound together with
the ubiquitous use of the R software. [...] Not only is this a very
brave attempt, the author succeeds in it exceptionally well. It is
a very easy read: I read the full text coverto-cover in just a few
days. [...] Overall this is an excellent text that I could happily
throw to any starting Ph.D. student/junior researcher who had
little-to-no experience in CLRV models."
-Mark N. Harris, in Journal of the American Statistical
Association, July 2023"Overall, the materials are presented in an
easy to comprehend manner, not only the main results and reviews of
the basics, but also their important variants and extensions. In
addition to the basic theories, the comments or narratives of their
origins and developments certainly help enhance the readability,
accessibility and interpretability of the materials, especially the
advanced parts. The illustrative examples, extensive R codes and
outputs, data analyses and their explanations in the doing part are
extremely useful. The presentation of both Bayesian and frequentist
approaches in a parallel manner provides additional value to the
reader. Each chapter can be read or studied independently, and
there is a website devoted to the relevant R code with
supplementary notes. I enjoyed reading the book. It is a very
welcome addition to the literature, and is especially handy for
those who study or work with regression modelling and data
analytics."
-Shuangzhe Liu, in International Statistical Review, June 2023"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, University of Notre Dame"This Chapman & Hall
book by Jun Xu is a thorough introduction to a range of generalized
linear or categorical response variable (termed limited response
variable in the book) models that will benefit data analysts
focused on applications. As an author who has written on such
models including two books, I find this new book a treasure for the
following reasons. First, the book provides both the Bayesian and
the frequentist treatments of generalized linear or categorical
response variable models in the main chapters, each of which deals
with one type of such models. Second, the author uses graphics and
R and rstan/rstanarm code profusely and effectively to enhance
learning by way of real-world application examples. Third, the
book, while giving the reader a good exposure to a range of models,
is particularly strong in the presentation of polytomous regression
such as models of ordered and of unordered response since the
author has done a good deal of research on models with ordered
response. Fourth, the text gives importance to essential
statistical tests, especially the likelihood ratio, score, and Wald
tests as well as tests of the parallel lines/proportional odds
assumption and of the proportional hazard assumption. Finally, the
books will be particularly useful for instructors of graduate-level
applied statistics on generalized linear models who primarily teach
in the frequentist tradition but will want to provide a Bayesian
alternative."
- Tim Liao, SUNY Stony Brook & University of Illinois
"Overall, the materials are presented in an easy to comprehend
manner, not only the main results and reviews of the basics, but
also their important variants and extensions. In addition to the
basic theories, the comments or narratives of their origins and
developments certainly help enhance the readability, accessibility
and interpretability of the materials, especially the advanced
parts. The illustrative examples, extensive R codes and outputs,
data analyses and their explanations in the doing part are
extremely useful. The presentation of both Bayesian and frequentist
approaches in a parallel manner provides additional value to the
reader. Each chapter can be read or studied independently, and
there is a website devoted to the relevant R code with
supplementary notes. I enjoyed reading the book. It is a very
welcome addition to the literature, and is especially handy for
those who study or work with regression modelling and data
analytics."
-Shuangzhe Liu, in International Statistical Review, June 2023"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, University of Notre Dame"This Chapman & Hall
book by Jun Xu is a thorough introduction to a range of generalized
linear or categorical response variable (termed limited response
variable in the book) models that will benefit data analysts
focused on applications. As an author who has written on such
models including two books, I find this new book a treasure for the
following reasons. First, the book provides both the Bayesian and
the frequentist treatments of generalized linear or categorical
response variable models in the main chapters, each of which deals
with one type of such models. Second, the author uses graphics and
R and rstan/rstanarm code profusely and effectively to enhance
learning by way of real-world application examples. Third, the
book, while giving the reader a good exposure to a range of models,
is particularly strong in the presentation of polytomous regression
such as models of ordered and of unordered response since the
author has done a good deal of research on models with ordered
response. Fourth, the text gives importance to essential
statistical tests, especially the likelihood ratio, score, and Wald
tests as well as tests of the parallel lines/proportional odds
assumption and of the proportional hazard assumption. Finally, the
books will be particularly useful for instructors of graduate-level
applied statistics on generalized linear models who primarily teach
in the frequentist tradition but will want to provide a Bayesian
alternative."
- Tim Liao, SUNY Stony Brook & University of Illinois
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