740—Statistical Computing. (3) (Prereq: STAT 713) A survey of current algorithms and software for solving fundamental problems of statistical computing with emphasis on computer generation of random variates.
Usually Offered: Alternating Fall Semesters
Purpose: To introduce graduate students to the major topics in computational statistical and statistical computing. To help the students build the programming skills needed for thesis or dissertation work.
Numerical Analysis for Statisticians, by K. Lange, Springer, 1998.
Numerical Methods of Statistics, by J.F. Monahan, Cambridge University Press, 2001.
An Introduction to the Bootstrap, by B. Efron & R.J. Tibshirani, Chapman & Hall, 1993.
Numerical Recipes in Fortran, by W.H. Press, S.A. Teukolsky, W.T. Vetterling, & B.P. Flannery, Cambridge University Press, 1992.
Monte Carlo Statistical Methods, by C.P. Robert & G. Casella, Springer, 1999.
S Programming, by W.N. Venables & B.D. Ripley, Springer, 2000.
|Programming Languages: R and Fortran||1 week|
|Random Number Generation: Generating Uniform Random Variables, Inverse Integral Transformation, Acceptance/ Rejection Method, importance sampling, Special Relationships, Fleishman's Power Method||2.5 weeks|
|Resampling Methods: Simulation Studies, Nonparametric Bootstrap, Jackknife, Parametric Bootstrap||2 weeks|
|Issues in Maximum Likelihood Estimation: Root Finding, Optimization, Constrained Optimization||2 weeks|
|EM Algorithm||2 weeks|
|Markov Chain Monte Carlo: Markov Chains, Metropolis-Hastings, Gibbs Sampling, Convergence||2.5 weeks|
|Smoothing Methods: Kernel Smoothing, Spline Smoothing, Fast Fourier Transform, Wavelets, Density Estimation||2 weeks|
The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.