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Dominique Bontemps

  1. About adaptive coding on countable alphabets.

    Чт, 02/02/2012 - 15:01 — SomNambul
    Authors: Dominique Bontemps, Elisabeth Gassiat, Stéphane Boucheron
    Subjects: Statistics
    Abstract

    This paper sheds light on universal coding with respect to classes of
    memoryless sources over a countable alphabet defined by an envelope function
    with finite and non-decreasing hazard rate. We prove that the auto-censuring AC
    code introduced by Bontemps (2011) is adaptive with respect to the collection
    of such classes. The analysis builds on the tight characterization of universal
    redundancy rate in terms of metric entropy % of small source classes by Opper
    and Haussler (1997) and on a careful analysis of the performance of the
    AC-coding algorithm.

  2. Bernstein von Mises Theorems for Gaussian Regression with increasing number of regressors.

    Ср, 09/08/2010 - 15:01 — SomNambul
    Authors: Dominique Bontemps
    Subjects: Statistics
    Abstract

    This paper brings a contribution to the Bayesian theory of nonparametric and
    semiparametric estimation. We are interested in the asymptotic normality of the
    posterior distribution in Gaussian linear regression models when the number of
    regressors increases with the sample size.

  3. A new penalized criterion for variable selection and clustering using genotypic data.

    Пнд, 02/08/2010 - 16:01 — SomNambul
    Authors: Dominique Bontemps, Wilson Toussile
    Subjects: Statistics
    Abstract

    We consider the problem of estimating the number of components and the
    relevant variables in a mixture model for multilocus genotypic data. A new
    penalized maximum likelihood criterion is proposed, and a non-asymptotic oracle
    inequality is obtained. Further, under weak assumptions on the true probability
    underlying the observations, the selected model is asymptotically consistent.
    On a practical aspect, the shape of our proposed penalty function is defined up
    to a multiplicative constant which is calibrated thanks to the slope
    heuristics, in an automatic data-driven procedure.

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