SATAISTICAL
COMPUTING
SPRING 2013
Instructor: 
GuanHua Huang, Ph.D. 

Office: 423 Joint Education Hall 

Phone: 035131334 

Email: ghuang@stat.nctu.edu.tw 
Class meetings: 
Tuesday 9:0012:00 at 407 Joint Education Hall 
Office hours: 
By
appointment 
Class website: 

Credit: 
Three (3) credits 
This course will introduce topics in numerical
analysis useful for statistical modeling and analysis. Topics include computer programming,
random number generation, Monte Carlo simulation, permutation test and the
bootstrap, numerical linear algebra, the EM algorithm, optimization, numerical integration,
hidden Markov models, and Markov chain Monte Carlo.
Handouts corresponding to each lecture
will be available on the class website before each class. Reading assignments are from the following two books:
Ÿ
Lange K (2010). Numerical
Analysis for Statisticians, 2nd edition. Springer.
Ÿ
Venables WN and Ripley BD (2002). Modern
Applied Statistics with S, 4th edition. Springer.
Students are
expected to have background on undergraduate probability, and mathematical
statistics. Computer programming knowledge on R/SPlus/Matlab and/or C/C++ is
required.
The course grade will be based on four to five homework assignments (50%), one midterm
exam (20%),
and one final exam (30%).
COURSE OUTLINE
Lange K
(2010). Numerical Analysis for Statisticians, 2nd edition.
Springer. (NAS)
Venables WN and Ripley BD (2002). Modern Applied Statistics with S, 4th edition. Springer. (MASS)
Module 
Topic 

1 
Introduction;
R 
MASS Chapters 14 The R
manuals: 
2 
Linux;
LaTeX 

3 
Random
number generation 
NAS Chapter 22 MASS Section 5.2 
4 
Permutation
test and the bootstrap 
NAS Chapter 24 
5 
Numerical
linear algebra 
NAS Chapters 89 
6 
EM
algorithm 
NAS Chapter 13 
7 
Optimization:
NewtonRaphson, Fisher scoring 
NAS Chapter 14 
8 
Nonlinear
regression, iteratively reweighted least squares 
NAS Sections 14.6 and 14.7 
9 
EM
algorithm extensions 
NAS Chapter 13 
10 
L_{p}
regression and constrained optimization 
NAS Chapter 11 
11 
Numerical
integration 
NAS Chapter 18 
12 
Hidden
Markov models 
NAS Section 25.3 
13 
Markov
chain Monte Carlo I 
NAS Chapter 26 
14 
Markov
chain Monte Carlo II 
NAS Chapter 27 