Course Homepage: Spring 2004

Usually Offered: Alternating Spring 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.

Recommended Texts:

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.

 

Topics Covered	Time
Programming Languages: R and Fortran	1 week
Computer Arithmetic: Integers and Floating Point Representations	1 week
Random Number Generation: Generating Uniform Random Variables, Inverse Integral Transformation, Acceptance/ Rejection Method, Special Relationships, Fleishman's Power Method	2 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 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.  

Contact Faculty: Brian Habing, John Grego
(Last Updated: July 11, 2008)