| Course Name |
(中) 遺失資料統計方法(6058) |
| (Eng.) Statistical Methods with Missing Data |
| Offering Dept |
Graduate Institute of Statistics |
| Course Type |
Elective |
Credits |
3 |
Teacher |
LIN, TSUNG-I |
| Department |
Graduate Institute of Statistics / Graduate |
Language |
Chinese |
Semester |
2026-SPRING |
| Course Description |
This course aims to introduce commonly used and modern methods for handling missing data in statistical analyses. The course covers naive approaches and their limitations, missing data mechanisms and assumptions, likelihood-based methods, imputation strategies, inverse-probability weighting, selection models, sensitivity analysis, and recent developments for nonignorable missingness. Key computational tools, including the Expectation–Maximization algorithm, Gibbs sampling, and MCMC techniques, will also be introduced. |
| Prerequisites |
|
| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| Understand the fundamental theories and methods related to missing data. |
|
|
| Exercises |
| Discussion |
| Lecturing |
|
| Written Presentation |
| Attendance |
| Oral Presentation |
| Assignment |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
General Concepts of Missing Data |
| Week 2 |
Simple methods of Missing Data |
| Week 3 |
EM algorithm |
| Week 4 |
ECM and ECME algorithms |
| Week 5 |
AECM algorithm |
| Week 6 |
Likelihood-based Methods under Missingness |
| Week 7 |
Likelihood-based Methods under Missingness |
| Week 8 |
Multiple Imputation Methods under Missingness |
| Week 9 |
Multiple Imputation Methods under Missingness |
| Week 10 |
Longitudinal data with missing value |
| Week 11 |
Longitudinal data with missing value |
| Week 12 |
Sensitivity Analysis for Missing Data |
| Week 13 |
Gibbs and Slice samplers |
| Week 14 |
MCMC algorithms |
| Week 15 |
Final Project Presentations |
| Week 16 |
Final Project Presentations |
self-directed
learning |
02.Viewing multimedia materials related to industry and academia.
|
|
| Evaluation |
| Homework 30%, Midterm 30%, Final Project Presentations 40% |
| Textbook & other References |
|
| Teaching Aids & Teacher's Website |
| 自編講義與教材 |
| Office Hours |
| Appointment |
| Sustainable Development Goals, SDGs(Link URL) |
| 01.No Poverty 02.Zero Hunger 03.Good Health and Well-Being 04.Quality Education 08.Decent Work and Economic Growth | include experience courses：N |
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