30204 - Modern Applied Regressions: Bayesian and Frequentist Analysis of Categorial and Limited Response Variables with R

Description

This workshop introduces Bayesian analysis by comparing it with the traditional frequentist inference under the general regression framework. The discussion will focus on the conceptual ideas of Bayesian and frequentist estimations, their applications with classical generalized regression models, and easy interpretation of results using R and Stan.

This workshop plans to cover the following sub-topics,
1. Thomas Bayes, Bayes’s Theorem, and Bayesian Inference
2. Classical Linear Regression Models
3. Bayesian Analysis of Linear Regression Models
4. Bayesian Analysis of Binary Regression Models
5. Advanced Regression Models

The learning objectives that I plan to accomplish after my workshop include,
1. Participants understand the conceptual ideas behind Bayesian estimation and inference
2. Participants can run simple Bayesian regression models with R
3. Participants can interpret the results, especially those from Bayesian analysis with R and Stan
4. Participants understand the difference between Bayesian and frequentist methods
5. Participants know the resources for continuous education on this topic

Instructor

Jun Xu, PhD
Professor and Head
Department of Sociology
Faculty of Social Sciences
University of Macau
Macau, SAR, China

Qualification

I am a full professor of sociology at the University of Macau. In addition to sociological inquiries, my quantitative research/teaching interests include Bayesian statistics, categorical data analysis, causal inference, machine learning, and statistical programming. My methodological works have appeared in journals such as Sociological Methods and Research, Social Science Research, and The Stata Journal. I am the author of Modern Applied Regressions: Bayesian and Frequentist Analysis of Categorical and Limited Response Variables with R and Stan, published by Chapman and Hall of Taylor & Francis Group. This is the book used as the basis for this workshop. I am also a co-author (with Dr. Andrew S. Fullerton) of Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives, by the same publisher.

Sub Unit

• Workshops / Professional Development Workshop

Presenter

• Jun Xu, University of Macau

Session Organizer

• Jun Xu, University of Macau