STAT 633: Statistical Computing

Spring 2019

Time & Place: 5:30-6:45pm TTh in SOND 206
Office Hours: 3:30-5:30pm TTh or by appointment in Math/Psych 411

Instructor: Seungchul Baek

Syllabus (.pdf)

Textbook: Statisical Computing with R, 1st edition. (2008), by Maria L. Rizzo.
Reference: Computational Statistics, 2nd edition. (2013), by Geof H. Givens and Jennifer A. Hoeting.

Lectures:

• Tues., Jan. 29: Class cancelled due to weather confidtion.
• Thur., Jan. 31: Syllabus, introduction to STAT 633, preliminary test.
• Tues., Feb. 5: Convergence rate, Newton-Raphson, secant method.
• Thur., Feb. 7: Bisection method, fixed-point method, introduction to optimization.
• Tues., Feb. 12: Multivariate Newton, Newton-like methods.
• Thur., Feb. 14: Gauss-Newton, numerical integration methods, convexity.
• Tue., Feb. 19: EM algorithm.
• Thur., Feb. 21: Finished EM algorithm.
• Tue., Feb. 26: Chapter 3—generating random numbers.
• Thur., Feb. 28: Chapter 3 continued.
• Tue., Mar. 5: Finished Ch 3, Chapter 5—Monte Carlo integration.
• Thur., Mar. 7: Chapter 5-II —Variance reduction.
• Tue., Mar. 12: Chapter 6 — MC inference.
• Thur., Mar. 14: Chapter 7 —Bootstrap and Jackknife.
• The., Mar. 19: Spring Break, no class.
• Thur. Mar. 21: Spring Break, no class.
• Tue., Mar. 26: Reading day for midterm, no class.
• Thur., Mar. 28: Finished Chapter 7.
• Tue. Apr. 2: Markov chain--Definitions, examples, Chapman-Kolmogorov equations, etc.
• Thur. Apr. 4: Finished Markov chain--Classification of states, ergodicity. Gibbs sampler, Examples 1 & 2.
• Tue. Apr. 9: Gibbs sampler, Examples.
• Thur. Apr. 11: Convergence of Gibbs sampler, Section 2--Examples 3 & 4.
• Tue. Apr. 16: Bayesian analysis and Gibbs sampler--Normal model Section 3--Examples.
• Thur. Apr. 18: Bayesian analysis and Gibbs sampler--Change-of-point dectection.
• Tue. Apr. 23: Probit/Logisitic model, data in Example 3. Metropolis-Hastings sampler, Examples in Section 1.
• Thur. Apr. 25: Metropolis-Hastings sampler, Example in Section 2, Example in Section 3, Examples in other sections.
• Tue. Apr. 30: Diagnosis of Metropolis-Hastings sampler. Slice Sampler, Example.

Exams:

• Midterm Due Thursday, March 28. (MC)EM algorithm for #4. Code for #4. Solution for #3 (c).
• Final Due 5:00pm Friday, May 17.

Project:

• Proposal: Due Thursday, March 28.
• Final report: Due 5:00pm Firday, May 10.
• Presenation:
  - Thur., May 2: Neha Agarwals, Gaurab Hore
  - Tue., May 7: Michael Lucagbo, Mark Romos
  - Thur., May 9: Sudheer Shukla, Jing Wang
  - Tue., May 14: James Whitehead
• Papers' list that may be chosen for your project.

Homework Assignments:

• Howework 1: Due Tuesday, February 19.
• Howework 2: Due Thursday, March 7. (Data and description for Problem 1.) Solution for #2.
• Howework 3: Due Thursday, March 28.
• Howework 4: Due Tuesday, April 16.
• Howework 5: Due Tuesday, May 2.

Links

• The R Project for Statistical Computing
• "An Introduction to R" (manual)
• R Studio: a convenient R programming environment
• Brief tutorial of R markdown by Roger Peng