Stat 104 - “Re-sampling methods in statistical modeling”

Pr. G. Bontempi


The course will introduce computer intensive methods of statistical analysis and their application to statistical modeling.
Computer-intensive 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.




Course (15h)

  1. Introduction
  2. Basic concepts of statistical inference
  3. Re-sampling methods for estimation and testing
  4. Modeling and Data analysis.
  5. Re-sampling methods for model assessment and selection.
  6. Model selection and model averaging



Matlab and R scripts (see slides for information)


Internet support


Bootstrap Methods: Another Look at the Jackknife  
B. Efron
Annals of Statistics, Vol. 7, No. 1. (Jan., 1979), pp. 1-26.

A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation
Bradley Efron; Gail Gong
The American Statistician, Vol. 37, No. 1. (Feb., 1983), pp. 36-48.

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. 54-75.

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. 1261-1295.

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. 1301-1304.

Combining Estimates in Regression and Classification
Michael LeBlanc; Robert Tibshirani
Journal of the American Statistical Association
, Vol. 91, No. 436. (Dec., 1996), pp. 1641-1650.

Heuristics of Instability and Stabilization in Model Selection
Leo Breiman
The Annals of Statistics
, Vol. 24, No. 6. (Dec., 1996), pp. 2350-2383.

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 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: The MathWorks
The MathWorks -page francaise
Octave, un clone gratuit de Matlab
 Tutorial Matlab

A practical introduction to MATLAB
Books and Tutorials on MATLAB
Tutorial MATLAB
Open source project MatLinks/Chorus on Matlab/Octave