MATH 3426 Sampling

Syllabus (Spring, 2019, HKUST)

Beginning Stories [LectureNote]
- Assigned Reading: The preface of "lady tasting tea"
- Suggested Reading:
  - The Literary Digest Poll: Making of a Statistical Myth.
  - Which Polls Fared Best (and Worst) in the 2012 Presidential Race
Chapter 1. Overview of the sampling and survey process [LectureNote]
- An example of survey data analysis [data][Rmarkdown][html]
- ggplot2 tutorial
- Suggested Reading:
  - Two Brains Running
  - Book "Thinking, Fast and Slow" by Daniel Kahneman
Chapter 2. Simple Random Sampling [LectureNote]
- R code: The confidence interval visualised by ggplot2.
- R code: Normal distribution vs. t-distribution.
- Delta Method [R code for an example in the lecture note]
Chapter 3. Stratified Simple Random Sampling [LectureNote]
Chapter 4. Systematic sampling [LectureNote]
Chapter 5. Cluster sampling [LectureNote]
Chapter 6. James-stein Estimator and Empirical Bayes [LectureNote]
Chapter 7. Linear mixed models [LectureNote]
Chapter 8. Introduction to Linear Mixed Models with lmer [pdf][Rmarkdown][dataset]

Epilogue: where to go from here

Overview of Statistical learning [notes]
An Introduction to Statistical Learning [On-line course by Hasite and Tibshirani]
Learning from Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin. [On-line course by Yaser S. Abu-Mostafa]
Doing Bayesian Data Analysis. By John K. Kruschke.
The book "Model-based machine learning" by John Winn and Christopher Bishop. [website]
The book “Pattern Recognition and Machine Learning” [link] by Christopher Bishop

Tutorials by our TA, Mr Mingxuan Cai.

Assignment

Project. [pdf] Due date: May 28, 2019. [Suggested solution results]

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