Computational Statistics

Instructor

• Nan Zhang

Textbook

• Rizzo (2019). Statistical Computing with R, 2nd Edition, CRC

Reference

• Givens and Hoeting (2012). Computational Statistics, Wiley
• Matloff (2011). The Art of R Programming: A Tour of Statistical Software Design, No Starch Press
• Wickham, Çetinkaya-Rundel, and Grolemund. (2023) R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd Edition, O’Reilly Media

Schedule

• Week 1: Introduction to R. Probability and Statistics Review.
• Week 2: Methods for Generating Random Variables.
• Week 3: Methods for Generating Random Vectors.
• Week 4: Monte Carlo Integration. Antithetic Variables.
• Week 5: Control Variates. Importance Sampling.
• Week 6: Stratified Sampling.
• Week 7: Monte Carlo Methods in Estimation.
• Week 8: Monte Carlo Methods in Hypothesis Testing.
• Week 9: Midterm.
• Week 10: The Bootstrap. Bootstrap Confidence Intervals.
• Week 11: The Jackknife. Cross Validation.
• Week 12: Introduction to Bayesian Analysis. Markov Chain Monte Carlo Methods.
• Week 13: The Metropolis-Hastings Algorithm.
• Week 14: The Gibbs Sampler. Monitoring Convergence.
• Week 15: EM Algorithm.
• Week 16: Variational Inference.