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POMMERET Denys

Organisme : Aix Marseille Université
Professeur
(HDR)

Equipe: Statistiques (STA) (responsable de)

Autre(s) thème(s) de recherche ou rattachement(s) :

denys.pommeret _AT_

univ-amu.fr

04 91 26 96 62

Bureau: 131, Etg: 1, Bât: TPR2 - Site : Luminy

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Dernieres productions scientifiques :

A test for the equality of transformations of two random variables

Auteur(s): Boutahar Mohamed, Pommeret Denys

(Article) Publié: Esaim: Probability And Statistics, vol. p. (2016)

Ref HAL: hal-01293391_v1
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Data driven smooth test of comparison for dependent sequences

Auteur(s): Doukhan P., Pommeret D., Reboul L.

(Article) Publié: Journal Of Multivariate Analysis, vol. 139 p.147-165 (2015)

Ref HAL: hal-01287763_v1
DOI: 10.1016/j.jmva.2015.02.017
Exporter : BibTex | endNote
Résumé:

In this paper we propose a smooth test of comparison for the marginal distributions of strictly stationary dependent bivariate sequences. We first state a general test procedure and several cases of dependence are then investigated. The test is applied to both simulated data and real datasets.

A polynomial expansion to approximate the ultimate ruin probability in the compound Poisson ruin model

Auteur(s): Goffard Pierre-Olivier, Loisel Stéphane, Pommeret Denys

(Document sans référence bibliographique) 2014-00-00

Ref HAL: hal-00853680_v2
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Résumé:

A numerical method to approximate ruin probabilities is proposed within the frame of a compound Poisson ruin model. The defective density function associated to the ruin probability is projected in an orthogonal polynomial system. These polynomials are orthogonal with respect to a probability measure that belongs to Natural Exponential Family with Quadratic Variance Function (NEF-QVF). The method is convenient in at least four ways. Firstly, it leads to a simple analytical expression of the ultimate ruin probability. Secondly, the implementation does not require strong computer skills. Thirdly, our approximation method does not necessitate any preliminary discretisation step of the claim sizes distribution. Finally, the coefficients of our formula do not depend on initial reserves.

Testing the mechanism of missing data

Auteur(s): Pommeret Denys

(Document sans référence bibliographique) 2012-02-00

Ref HAL: hal-00669339_v1
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Résumé:

We consider the problem of missing data when the mechanism of missingness is not at random and when the partially observed vari- able has known or observed moments. A nonparametric estimator of the probability of missingness is proposed. A data driven statistic is constructed to test the missingness mechanism. Illustrations through univariate logistic regressions are presented: the method permits to estimate regression coe cients when the covariate is completely miss- ing for one response category. A test of signi cance is proposed for the coe cients. The performance of the method is investigated in a simulation study. An illustration is considered using a real data set.

Imputation by PLS regression for generalized linear mixed models

Auteur(s): Guyon Emilie, Pommeret Denys

(Document sans référence bibliographique) 2011-12-08

Ref HAL: hal-00650295_v1
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Résumé:

The problem of handling missing data in generalized linear mixed models with correlated covariates is considered when the missing mechanism concerns both the response variable and the covariates. An imputation algorithm combining multiple imputation and Partial Least Squares (PLS) regression is proposed. The method relies on two steps. In a first step, using a linearization technique, the generalized linear mixed model is approximated by a linear mixed model. A latent variable is introduced and its associated PLS components are constructed. In a second step these PLS components are used in the generalized linear mixed model to impute the response variable. The method is applied on simulations and on a real data.

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