This course aims to study the basic and standard sampling design and estimation methods, especially the methods for populations that are inherently difficult to sample. The topics include: Basic random variable sampling, Conditional expectation, Monte Carlo algorithm, Expectation-Maximum algorithm, Resampling (cross validation, bootstrap), Linear regression, Maximum likelihood, Monte Carlo Makov Chain. The software to be used in this course is Python.
Upon completion of this course, students are expected to be clear with basic ideas and motivations for sampling, some basics sampling methods and statistical estimation methods, and understand advanced sampling methods. Students will be able to use these sampling methods to analyze real data sets.
- Teacher: hwyuan, Yuan Hongwei