
STA410S/2102S: Statistical computation
New: HW 3 due on April 11 before 5 pm; turn in to SS 6018 (Stats Dept
Office)
Latest
 There is a mistake in my expression for the loglikelihood
for question 3(a): there is a missing term involving pi, the probability
that Delta = 1. An amended version of the homework
appears here.
 Solutions for test 2.
 Homework 3 is not due until April 11.
 This file gives the calcium data needed for question 1 in a format easier to read into R.
Meets in Lash Miller, 158, Tuesday, Thursday and Friday at 1.
**Note On Fridays given jointly with STA 450S.
Course Information
This course will study how statistical computations are done, and
develop students' abilities to write programs for statistical problems
that are not handled by standard packages. Students will learn the
capabilities of the R statistical computing environment, and
learn to program new statistical methods in that environment.
R will be introduced as part of the course; no prior knowledge of
it is necessary.
Prerequisites: This course is designed for graduate and senior
undergraduate students in statistics, actuarial science, computer
science or other fields where statistical computation is important.
Students should have a basic background in statistical methods
(eg. at the level of STA302), and some prior experience with
programming (eg. at the level of CSC108).
Textbook: Venables and Ripley. Modern Applied Statistics with S, 4th ed.
SpringerVerlag.
Book web
page
Computing:
Assignments will be done in R. Graduate students will use the
Statistics/Biostatistics
computer system. Undergraduates will use CQUEST.
You can request an account
on CQUEST if you're an undergraduate student in this course.
You can also use R on your home computer by downloading it for free from http://probability.ca/cran/
Grading scheme:
Regular homework, worth 70%
Two onehour inclass tests, each worth 15%.
The tests are scheduled for February 22 and March 22.
Syllabus:
Chapters 13 of the text will serve as a resource/manual for using R. Our
emphasis will be on Chapters 58, to illustrate a number of advanced
statistical methods that are available through R. We will also discuss
numerical and algorithmic concepts that are of particular relevance to
statistics, including solution of linear systems, nonlinear optimization, and
approximation of integrals. Special topics from Chapters 10, 13 and 14 will be covered as time and interests permit.
March 29, 2005
 Handout on survival data (following Chapter 13 of VR)
March 24, 2005
 Handout on mixed effects models (following Chapter 10.1 of VR)
March 11, 2005
 Notes on nonlinear least squares
March 10, 2005
March 8, 2005
 Notes on methods of optimization
 Worth also looking at the help files for uniroot and optim
March 3, 2005
 Notes on onedimensional rootfinding.
 Radford Neal's program for iteration, NewtonRaphson, scoring,
for truncated Poissson.
 Some output illustrating its use.
 you should also look at the R help file for uniroot.
March 1, 2005
 Notes on likelihood inference.
February 24, 2005
 Notes on inference for generalized linear models.
February 22, 2005
February 8, 2005
 Notes on estimation of regression parameters in generalized linear models by iteratively reweighted least squares.
 Handout of R code.
 Practice problems for Test 1.
February 3, 2005
 Notes on generalized linear models.
February 1, 2005
 Notes on density estimation.
January 28, 2005
January 27, 2005
 Notes on robust regression.
January 25, 2005
January 20, 2005
January 18, 2005
January 14, 2005
 Data for Example G from Cox and Snell
 R code handout
January 11 and 13, 2005
 Annotated R code for Jan 13
 On Friday we will meet in LM 158 (the usual place) for a tutorial on
lm, the function that fits linear models in R.

Handwritten lecture notes for Jan 11,13.
(Warning: they will print in color unless you ask for black and white.)
January 6, 2005
January 4, 2005
