Statistical Computing

(Est. February 2004, Revised:  3/15/2004)

Prerequisite: One year graduate level  mathematical statistics courses at the level of Casella and Berger's book entitled Statistical Inference.

課程內容

 June 2 Project presentation 9 Project presentation

Bootstrap, Cross-Validation
Maximum Likelihood, EM algorithm, Missing Data,
Step by step multivariate regression, robust regression,
Non-parametric Regression, Alternate Conditional Expectation, Projection Pursuit,
(Neural Nets)
Principal Components, Correspondence Analysis,
Classification and Regression Trees, Clustering,
Multiple response methods :PC-Instrumental Variables, Partial Least Squares,
Non parametric methods for Longitudinal Matrices, Conjoint Analysis,
Nonparametric Confidence Regions, (convex hulls),
Approximate Counting, Exhaustive Enumeration, Non parametric Tests.

2. Algorithms

• The linear equation solving problem:
• Matrix manipulations, decompositions, (cholesky, QR,...),
• Singular Value Decompositions, Eigenanalysis,
• Iterative methods, Gauss-Seidel, Conjugate Gradient,
• Downhill Simplex minimization,
• Smoothing,
• Random Number Generation ,
(Uniform, Normal, Beta, Inverse CDF, Acceptance Rejection, Metropolis),
• Markov Chain approach to Combinatorial Optimization (Simulated Annealing), Updating,
• Markov Chain Monte Carlo, Sorting.

3. Software

成績評量方式

• 習題（50%
• 劃（50%

Refer to webpage of Computational Statistics taught by Prof. Gentle at George Mason University on Students' class project
http://science.gmu.edu/\$\sim\$jgentle/csi771/02f/

Homework

• Homework should be turned in on time. No late homework will be accepted without legitimate reasons.
• You are expected to write your own codes and turn in your source code. Do not copy. Never ask your friends to write programs for you.