Modern Applied Regressions creates an intricate and colorful mural with
mosaics of categorical and limited response variable (CLRV) models using
both Bayesian and Frequentist approaches. Written for graduate students,
junior researchers, and quantitative analysts in behavioral, health, and
social sciences, this text provides details for doing Bayesian and
frequentist data analysis of CLRV models. Each chapter can be read and
studied separately with R coding snippets and template interpretation
for easy replication. Along with the doing part, the text provides basic
and accessible statistical theories behind these models and uses a
narrative style to recount their origins and evolution.
This book first scaffolds both Bayesian and frequentist paradigms for
regression analysis, and then moves onto different types of categorical
and limited response variable models, including binary, ordered,
multinomial, count, and survival regression. Each of the middle four
chapters discusses a major type of CLRV regression that subsumes an
array of important variants and extensions. The discussion of all major
types usually begins with the history and evolution of the prototypical
model, followed by the formulation of basic statistical properties and
an elaboration on the doing part of the model and its extension. The
doing part typically includes R codes, results, and their
interpretation. The last chapter discusses advanced modeling and
predictive techniques--multilevel modeling, causal inference and
propensity score analysis, and machine learning--that are largely built
with the toolkits designed for the CLRV models previously covered.
The online resources for this book, including R and Stan codes and
supplementary notes, can be accessed at https:
//sites.google.com/site/socjunxu/home/statistics/modern-applied-regressions.
