This course is a reading
seminar in that all participating students
present and discuss one "classic", i.e. one of the important papers
that contributed to the advance of Statistics as a science.
The evaluation of the participating students is based (a) on the
understanding and presentation of the assigned paper, as well as (b) on
the
contribution to the other discussions. Attendance is thus compulsory
for the whole duration of the class. Unless argued otherwise prior to
the presentation, the presentation and discussions are in English.

**Contacts:**
Christian
Robert, Bureau **B638**,

tel. **01 4405 4335**

email `xian@ceremade.dauphine.fr`

- The estimation of location and scale parameters of a continuous population of any given form J. Pitman Biometrika (1939)
- Periodogram analysis and continous spectra, M.S.Bartlett Biometrika (1950)
- Testing for serial correlation in least square regression J. Durbin & G.S. Watson Biometrika (1950)
- Monte Carlo sampling methods using Markov chains and their applications, W.K.Hastings Biometrika (1970)
- The multiple recapture census for closed populations and
incomplete 2
^{k}contingency tables S.E. Fienberg Biometrika (1972) - On the mathematical foundations of theoretical statistics R.A. Fisher Philosophical Trans. Royal Statistical Society London (1922)
- On the problem of the most efficient test of statistical hypotheses J. Neyman & E.S. Pearson Philosophical Trans. Royal Statistical Society London (1933)
- Algorithm AS 136: A K-Means Clustering Algorithm. J. Hartigan & M. Wong Applied Statistics (1979)
- Regression models and life-table D.R. Cox J. Royal Statistical Society (1972)
- Bayes Estimates for the Linear Model D.V. Lindley & A.F.M. Smith J. Royal Statistical Society (1972)
- Generalized linear models Nelder, J.A. and Wedderburn, R.W. J. Royal Statistical Society (1972)
- Marginalisation paradoxes in Bayesian and structural inference A.P. Dawid, M. Stone & J. Zidek J. Royal Statistical Society (1973)
- Maximum likelihood from incomplete data via the EM algorithm A.P. Dempster, N.M. Laird and D.B. Rubin J. Royal Statistical Society (1977)
- Controlling the false discovery rate: a practical and powerful approach to multiple testing. Benjamini, Y. and Hochberg, Y. J. Royal Statistical Society (1995)
- Regression shrinkage and selection via the lasso R. Tibshirani J. Royal Statistical Society (1996)
- Bayesian measures of model complexity and fit D.J. Spiegelhalter, N.G. Best, B.P. Carlin, and A. van der Linde J. Royal Statistical Society (2002)
- On Rereading R.A. Fisher L. Savage Annals of Statistics (1976)
- Bootstrap methods: another look at the jacknife B. Efron Annals of Statistics (1979)
- Estimation of the mean of a multivariate normal distribution C. Stein Annals of Statistics (1981)
- Estimation of a bounded mean G. Casella & W. Strawderman Annals of Statistics (1981)
- Projection pursuit P.J. Huber Annals of Statistics (1985)
- Multivariate adaptive regression splines J. Friedman Annals
of Statistics (1991)

- On the Foundations of Statistical Inference A. Birnbaum J. American Statistical Assoc. (1962)
- How biased is the apparent error rate of a prediction rule? B. Efron J. American Statistical Assoc. (1986)
- Testing a point null hypothesis: the irreconciability of p-values and evidence J.O. Berger & T. Sellke J. American Statistical Assoc. (1987)
- Sampling-based approaches to calculating marginal densities A. Gelfand & A.F.M. Smith J. American Statistical Assoc. (1990)
- Adapting to unknown smoothness via wavelet shrinkage. D. Donoho & I. Johnstone J. American Statistical Assoc. (1995)
- A decision-theoretic generalization of online learning and an application to boosting Freund, Y. and Schapire, R.
*J. Computer and System Sciences* - A new look at the statistical model identification H. Akaike IEEE Transactions on Automatic Control (1974)
- Support-vector networks C Cortes and V Vapnik Machine learning (1995)