SATAISTICAL
COMPUTING
SPRING 2014
Instructor: 
GuanHua Huang, Ph.D. 

Office: 423 Joint Education Hall 

Phone: 035131334 

Email: ghuang@stat.nctu.edu.tw 
Class meetings: 
Friday 13:2016:20 at 406
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, nonlinear
regression, numerical integration, 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 three 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.
Ÿ
Rizzo ML (2007). Statistical
Computing with R. Chapman & Hall.
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.
There will
be homework assignments every week. The course
grade will be based on about 14 homework assignments (65%), one midterm
exam (15%),
and one final exam (20%).
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)
Rizzo ML
(2007). Statistical Computing with R. Chapman & Hall. (SCR)
Module 
Topic 

1 
Introduction
to statistical computing, R 
MASS Chapters 14 The R
manuals: http://cran.rproject.org/manuals.html
SCR Chapter 1 
2 
Introduction
to LATEX, Linux 
LaTeX
documentation: http://latexproject.org/guides/ Linux
introduction (Chinese): 
3 
Random
number generation 
NAS Chapter 22 MASS Section 5.2 SCR Chapter 3 
4 
Monte
Carlo methods in inference 
SCR Chapter 6 
5 
Bootstrap,
jackknife and permutation tests 
NAS Chapter 24 SCR Chapters 7 and 8 
6 
Numerical
linear algebra 
NAS Chapters 7, 8 and 9 
7 
EM
algorithm 
NAS Chapter 13 SCR Section 11.7 
8 
Optimization:
NewtonRaphson, Fisher scoring 
NAS Chapter 14 
9 
Nonlinear
regression, iteratively reweighted least squares 
NAS Sections 14.6 and 14.7 
10 
EM
algorithm extensions 
NAS Chapter 13 
11 
Constrained
optimization 
NAS Chapters 11 and 16 SCR Section 11.8 
12 
Numerical
integration 
NAS Chapter 18 SCR Section 11.3 
13 
Markov
chain Monte Carlo I 
NAS Chapters 25 and 26 SCR Chapter 9 
14 
Markov
chain Monte Carlo II 
NAS Chapters 25 and 26 SCR Chapter 9 