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.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.
Objective: Introducing statistical models for categorical data used by statistical researchers and practitioners.
Prerequisites:(a) Elementary Statistics(b).At least one of the following packages(SAS, R/Splus, or SPSS).
Contents :
1.Statistical inference for Two-way and Three-way Contigency tables under different assumptions.
2.Logit/Loglinear models and their extensions.
3.Generalized linear models with random effects for categorical responses.
4.Models checking and selection.
5.Asymptotic results and other advanced topics.
Sofewares:
1.SAS: PPRC FREQ, GENMOD, LOGISTIC, CATMOD, and NLMIXED.
2.S-PLUS or R: chisq.test, glm, fisher. test, gee, and glmmPQL.
3.SPSS: crosstabs, logistic, and plum.
Objective: Introducing statistical models for category data used by statistical researchers and practicers.
Prerequisites:(a) Elementary Statistics(b).At least one of the following packages(SAS, R/Splus, or SPSS).
Contents:
1.Statistical inference for Two-way and Three-way Contigency tables under different assumptions.
2.Logit/Loglinear models and their extensions.
3. Generalized linear models with random effects for category responses.
4.Models checking and selection.
5.Asymptotic results and other advanced topics.
Softwares:
1.SAS: PPRC FREQ, GENMOD, LOGISTIC, CATMOD, and NLMIXED.
2.S-PLUS or R: chisq.test, glm, fisher. test, gee, and glmmPQL.
3.SPSS: crossstabs, logistic, and plum.
“Categorical Data Analysis” by Alan Agresti
“Categorical Data Analysis” by Alan Agresti
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| Homework assignmentsHomework assignments Homework assignments |
20 | |
| Midterm, Quizzes, or ProjectsMidterm, Quizzes, or Projects Midterm, Quizzes, or Projects |
55 | |
| FinalFinal Final |
25 |
