Modern Applied Regressions
Bayesian and Frequentist Analysis of Categorical and Limited Response Variables with R and Stan
Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences;
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 8 December 2022
- ISBN 9780367173876
- Binding Hardback
- No. of pages286 pages
- Size 254x178 mm
- Weight 820 g
- Language English
- Illustrations 40 Illustrations, black & white; 29 Illustrations, color; 40 Line drawings, black & white; 29 Line drawings, color; 7 Tables, black & white 472
Categories
Short description:
Modern Applied Regressions creates an intricate 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.
MoreLong description:
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
MoreTable of Contents:
1. Introduction 2. Binary Regression 3. Polytomous Regression 4. Count Regression 5. Survival Regression 6. Extensions
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