PS 688 --- Winter 2005
This is the website for Applied Bayesian and Robust
Methods for Political Research, which is a course covering the basics
of some advanced methods of data analysis for graduate students in
the Political Science department of the University of Michigan.
Examples that I've worked out including Sweave files to recreate the analyses can be found here . The PDF files in this directory show all of the results of the analysis. The files that end with .rnw include both R code and LaTeX source to create the .pdf file (after running Sweave("filename.rnw") in R, and then processing the resulting .tex file with pdflatex. Right now the .txt files are datafiles.
Readings that are not online are available for
you to copy on the door of CPS 4253 or on CTools .
You can find the handouts (both the sources and the pdf versions) here
Readings and Supplementary Materials
Resampling and the Bootstrap
Bob Stine's lectures for his mini-course on the bootstrap are here
A great book on the topic, Bootstrap Methods and Their
by A.C. Davison and D.V. Hinkley.
And, of course, An Introduction to the Bootstrap
Some articles by Bradley Efron
Computers and the Theory of Statistics: Thinking the
Bradley Efron. SIAM Review, Vol. 21, No. 4. (Oct.,
1979), pp. 460-480.
This is a survey article concerning recent advances in certain
areas of statistical theory, written for a mathematical audience
with no background in statistics. The topics are chosen to
illustrate a special point: how the advent of the high-speed
computer has affected the development of statistical theory. The
topics discussed include nonparametric methods, the jackknife, the
bootstrap, cross-validation, error-rate estimation in discriminant
analysis, robust estimation, the influence function, censored
data, the EM algorithm, and Cox's likelihood function. The
exposition is mainly by example, with only a little offered in the
way of theoretical development.
A Leisurely Look at the Bootstrap, the Jackknife, and
Bradley Efron; Gail Gong. The American
Statistician, Vol. 37, No. 1. (Feb., 1983), pp. 36-48.
This is an invited expository article for The American
Statistician. It reviews the nonparametric estimation of
statistical error, mainly the bias and standard error of an
estimator, or the error rate of a prediction rule. The
presentation is written at a relaxed mathematical level, omitting
most proofs, regularity conditions, and technical details.
Interpretations of Probability
in the Foundations of Statistics
Bradley Efron. The American
Mathematical Monthly, Vol. 85, No. 4. (Apr., 1978), pp. 231-246.
Isn't Everyone a Bayesian?
B. Efron The American Statistician,
Vol. 40, No. 1. (Feb., 1986), pp. 1-5.
Originally a talk delivered at a conference on Bayesian statistics,
this article attempts to answer the following question: why is most
scientific data analysis carried out in a non-Bayesian framework? The
argument consists mainly of some practical examples of data analysis,
in which the Bayesian approach is difficult but Fisherian/frequentist
solutions are relatively easy. There is a brief discussion of
objectivity in statistical analyses and of the difficulties of
achieving objectivity within a Bayesian framework. The article ends
with a list of practical advantages of Fisherian/frequentist methods,
which so far seem to have outweighed the philosophical superiority of