107年第2學期-6192 類別資料分析 課程資訊

課程分享

選課分析

本課程名額為 70人,已有5人選讀,尚餘名額65人。

評分方式

評分項目 配分比例 說明
Homework assignments 20
Midterm, Quizzes, or Projects 50
Final 30

授課教師

蘇俊隆

教育目標

Introducing advanced statistical models and theories for categorical data used by statistical researchers and practitioners.1. Introduction: Distributions and Inference for Categorical Data 2. Describing Contingency Tables 3. Inference for Contingency Tables 4. Introduction to Generalized Linear Models 5. Logistic Regression 6. Building,Checking, and Applying Logistic Regression Models 7. Alternative Mideling of Binary Response Data 8. Models for Multinomial Responses 9. Loglinear Models for Contingency Tables 10. Building and Extending Loglinear Models 11. Models for Matched Pairs 12. Cluster Categorical Data: Marginal and Transitional Models 13. Cluster Categorical Data: Random Effect Models 14. Other Mixture Models for Discrete Data 15. Non-Model-Based Classification and Clustering 16. Large- and Small Sample Theory for Multinomial Models 17. Historical Tour of Categorical Data Analysis. In this semester, we may include some topics related to Data Mining such as Decision Trees, Bagging, Random Forests, and/or Boosting.

課程概述

Categorical data analysis that deals with qualitative or discrete quantitative data is one of the most important statistical tools nowadays. In recent years, this tool plays a fundamental role on analyzing polychotomous data, particularly in the social and health sciences. This course introduces statistical theories and models for analyzing categorical data. The main topics cover : (1) likelihood-based inferences on measures of association for two-dimensional and three-dimensional contingency tables under different assumptions. (2) generalized linear (mixed) models with emphasis on binary (Poisson) regression and logit models. (3) Repeated categorical data modeling, such as generalized estimating equation approaches and quasi-likelihood methods. (4) Asymptotic results and other advanced topics.

課程資訊

參考書目

“Categorical Data Analysis” by Alan Agresti