Modern Applied Regressions: Bayesian and Frequentist Analysis of Categorical and Limited Response Variables with R and Stan

Modern Applied Regressions is a book for graduate students, junior researchers, and quantitative analysts in behavioral, health, and social sciences that provides details for doing Bayesian and frequentist data analysis of CLRV models. It scaffolds both Bayesian and frequentist paradigms for regression analysis and discusses different types of categorical and limited response variable models, including binary, ordered, multinomial, count, and survival regression. The online resources for the book include R and Stan codes and supplementary notes.

Format: Hardback
Length: 272 pages
Publication date: 08 December 2022
Publisher: Taylor & Francis Ltd

Modern Applied Regressions is a comprehensive and visually captivating guide that delves into the realm of regression analysis, employing a diverse array of categorical and limited response variable (CLRV) models. Written with graduate students, junior researchers, and quantitative analysts in behavioral, health, and social sciences in mind, this text offers detailed insights into conducting both Bayesian and frequentist data analysis of CLRV models. Presented in a reader-friendly format, each chapter stands independently, allowing readers to explore and understand the topics at their own pace. Accompanied by R coding snippets and template interpretation, the book facilitates easy replication of the discussed methods.

In addition to providing practical guidance, Modern Applied Regressions delves into the foundational statistical theories underlying these models. Employing a narrative style, the text traces the origins and evolution of these models, making them accessible to a wide range of readers. The book begins by scaffolding both Bayesian and frequentist paradigms for regression analysis, providing a solid foundation for subsequent discussions. It then proceeds to explore a wide range of CLRV models, including binary, ordered, multinomial, count, and survival regression. Each middle chapter focuses on a specific type of CLRV regression, encompassing important variants and extensions.

The discussion of each major type begins with an overview of the model's historical context and evolution, followed by a detailed formulation of its basic statistical properties. The "doing" part of the model, encompassing R codes, results, and interpretation, is extensively covered, making it accessible to both novice and experienced researchers. The final chapter delves into advanced modeling and predictive techniques, such as multilevel modeling, causal inference and propensity score analysis, and machine learning, which are largely built upon the toolkits designed for CLRV models.

To enhance the learning experience, this book offers comprehensive online resources. These resources include R and Stan codes, as well as supplementary notes, allowing readers to further explore the topics and apply the learned methodologies to their research.

In conclusion, Modern Applied Regressions is a valuable resource for anyone seeking to understand and apply regression analysis with CLRV models. With its comprehensive coverage, accessible writing style, and practical examples, this text empowers researchers to analyze data and make informed conclusions in behavioral, health, and social sciences.

Weight: 794g
Dimension: 183 x 261 x 19 (mm)
ISBN-13: 9780367173876