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Department of Statistics

STAT 740

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.

Sample Course Homepage: Recent Semester

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.

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
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.  

Contact Faculty: John GregoBrian Habing, Lianming Wang

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