Pr.
G. Bontempi
20022003
Goal
The course will introduce computer intensive methods
of statistical analysis and their application to statistical modeling.
Computerintensive methods use resampling and repeated
simulations to calculate standard errors, confidence intervals and
significance tests or, more in general, to assess the quality of a
statistical models. These methods are not only in general use by statisticians
but are also applied by quantitative researchers in the life sciences,
social sciences and business. The methods apply for any level of modeling
and are well reputed for their easy understanding and implementation.
The course will emphasize the practical side of resampling methods
by illustrating the theoretical issues with practical applications to
data analysis, statistical modeling and machine learning.
The course will be supplemented by a set of computer based
examples (using the Matlab and the R language).
Prerequisites: basic notions of probability and statistics.
Staff
Schedule
 Thursday from 10 to 12 at room A2.222 (Campus La Plaine)
 First lesson (6th of March)
Content
Course (15h)
 Introduction
 Basic concepts of statistical inference
 Resampling methods for
estimation
and testing
 Bootstrap
 Parametric bootstrap
 Nonparametric bootstrap
 Permutation tests
 Randomization tests
 Bootstrap tests
 Modeling and Data analysis.
 Regression modeling
 Multiple linear regression
 Leastsquares.
 Empirical error and Final Prediction Error
 Nonlinear models (Supervised learning)
 Examples of nonlinear models
 Parametric identification
 Structural identification
 Model assessment and selection
 The bias/variance tradeoff
 Resampling methods
for model assessment and selection.
 Crossvalidation
 Leaveoneout.
 PRESS statistic
 Bootstrap
 The .632 bootstrap estimator
 Model selection and model averaging
Support
Software
Matlab and R scripts (see slides for
information)
Books
 B. Efron, R. Tibshirani (1993) An Introduction
to the bootstrap Chapman and Hall.
 T. Hastie, R. Tibshirani, J. Friedman (2001) The
Elements of Statistical Learning. Springer
 M. R. Chernick (1999) Bootstrap Methods: a practitioner's
guide Wiley.
 Duda,Hart,Stork (2001) Pattern Classification
(2nd ed). Wiley
 A.C. Davison, D.V. Hinkley (1997) Bootstrap Methods
and their Applications Cambridge University Press.
 J. Hjorth (1994) Computer Intensive Statistical
Methods Chapman & Hall.
Internet support
Articles
Bootstrap Methods: Another Look at the Jackknife
B. Efron
Annals of Statistics, Vol. 7, No. 1. (Jan., 1979), pp.
126.
A Leisurely Look at the Bootstrap, the Jackknife, and CrossValidation
Bradley Efron; Gail Gong
The American Statistician, Vol. 37, No. 1. (Feb., 1983),
pp. 3648.
Bootstrap Methods for Standard Errors, Confidence Intervals,
and Other Measures of Statistical Accuracy
B. Efron; R. Tibshirani
Statistical Science, Vol. 1, No. 1. (Feb., 1986), pp. 5475.
Jackknife, Bootstrap and Other Resampling Methods in Regression
Analysis
(in Invited Paper)
C. F. J. Wu
Annals of Statistics, Vol. 14, No. 4. (Dec., 1986), pp. 12611295.
Discussion: Jackknife, Bootstrap and Other Resampling Methods
in Regression Analysis
(in Invited Paper)
B. Efron
Annals of Statistics, Vol. 14, No. 4. (Dec., 1986), pp. 13011304.
Combining Estimates in Regression and Classification
Michael LeBlanc; Robert Tibshirani
Journal of the American Statistical Association, Vol. 91, No. 436.
(Dec., 1996), pp. 16411650.
Heuristics of Instability and Stabilization in Model Selection
Leo Breiman
The Annals of Statistics, Vol. 24, No. 6. (Dec., 1996), pp. 23502383.
Bias, Variance,
and Arcing Classifiers
Leo Breiman (1996)
Technical Report 460, Statistics Department, University of California
Bagging Predictors
Leo Breiman (1996)
Machine Learning
Web sites

Resampling Stats homepage

An Annotated Bibliography for Bootstrap Resampling
 Tibshirani's home
page
Boosting Research Site: Boosting.org

Boosting homepage

Combination of estimators: Volker Tresp's Home Page

Intelligent data analysis

Data mining glossary

Data Mining: a short tutorial
Statistical tools

The Comprehensive R Archive Network

R mailing lists archive

CRAN: R News

ESS  Emacs Speaks Statistics

R: Package Index

CRAN  Package Sources

CRAN: books Contributed Documentation
MATLAB
MATLAB: The MathWorks
The MathWorks page francaise
Octave, un clone gratuit de Matlab
Tutorial Matlab
US NAVY MATLAB tutorial
A practical introduction to MATLAB
Books and Tutorials on MATLAB
Tutorial MATLAB
Open source project MatLinks/Chorus on Matlab/Octave