Statistics Program

Spring 2004 Course

Statistical Computing

  • Course number : 221 U2000
  • Credits : 3 units
  • Required or Requisite : Requisite
  • Instructor : Hung Chen
  • Time : W 234
  • Room : N104
  • Notice :
  • Prerequisites : Regression analysis, and Advanced Statistical Inference (I), (II).
  • Course Description :
    This course is about modern, computationally-intensive methods in statistics. It emphasizes the role of computation as a fundamental tool of discovery in statistical analysis.
    1. Monte Carlo methods for statistical inference.
    2. Data partitioning and resampling (bootstrap).
    3. Numerical methods in statistics ("statistical computing").
    4. Graphical methods in computational statistics.
    5. Exploring data density and structure.
    6. Statistical models and data fitting.
  • Textbook :
    Lectures.
  • Reference :
  1. Gentle, J.E. (2002) Elements of Computational Statistics. Springer.
  2. Hastie, T., Tibshirani, R. and Friedman, J. (2001) The Elements of Statistical Learning: Data Mining, Inference, and Prediction.Springer-Verlag.
  3. Robert, C.P. and Casella, G. (1999) Monte Carlo Statistical Methods. Springer Verlag.
  4. An Introduction to R by William N. Venables, David M. Smith. (You can download lectures from http://www.ats.ucla.edu/stat/books/#DownloadableBooks)
  • Grading :
    Homework(50%), Project report(50%).

Last updated: February 26, 2004

Address: Department of Mathematics, National Taiwan University, Taipei, Taiwan
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