Adaptive Covariance Estimation with model selection

We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud

Statistical M-Estimation and Consistency in Large Deformable Models for Image Warping

The problem of defining appropriate distances between shapes or images and modeling the variability of natural images by group transformations is at the heart of modern image analysis. A current trend is the study of probabilistic and statistical aspects of deformation models, and the development of consistent statistical procedure for the estimation of template images. In this paper, we consider a set of images randomly warped from a mean template which has to be recovered. For this, we define an appropriate statistical parametric model to generate random diffeomorphic deformations in two-dimensions. Then, we focus on the problem of estimating the mean pattern when the images are observed with noise. This problem is challenging both from a theoretical and a practical point of view. M-estimation theory enables us to build an estimator defined as a minimizer of a well-tailored empirical criterion. We prove the convergence of this estimator and propose a gradient descent algorithm to compute this M-estimator in practice. Simulations of template extraction and an application to image clustering and classification are also provided

Non parametric estimation of the structural expectation of a stochastic increasing function

International audienceThis article introduces a non parametric warping model for functional data. When the outcome of an experiment is a sample of curves, data can be seen as realizations of a stochastic process, which takes into account the variations between the different observed curves. The aim of this work is to define a mean pattern which represents the main behaviour of the set of all the realizations. So, we define the structural expectation of the underlying stochastic function. Then, we provide empirical estimators of this structural expectation and of each individual warping function. Consistency and asymptotic normality for such estimators are proved

Wavelet penalized likelihood estimation in generalized functional models

The paper deals with generalized functional regression. The aim is to estimate the influence of covariates on observations, drawn from an exponential distribution. The link considered has a semiparametric expression: if we are interested in a functional influence of some covariates, we authorize others to be modeled linearly. We thus consider a generalized partially linear regression model with unknown regression coefficients and an unknown nonparametric function. We present a maximum penalized likelihood procedure to estimate the components of the model introducing penalty based wavelet estimators. Asymptotic rates of the estimates of both the parametric and the nonparametric part of the model are given and quasi-minimax optimality is obtained under usual conditions in literature. We establish in particular that the LASSO penalty leads to an adaptive estimation with respect to the regularity of the estimated function. An algorithm based on backfitting and Fisher-scoring is also proposed for implementation. Simulations are used to illustrate the finite sample behaviour, including a comparison with kernel and splines based methods

Tensions de rôle et stratégies d'ajustement : une étude auprès de cadres de santé à l'hôpital

International audienc

Tensions de rôle et stratégies d'ajustement : une étude auprès des cadres de santé

Depuis ces deux dernières décennies, la modernisation du secteur hospitalier implique un nouveau mode d’organisation de l’activité (externalisation des activités de support, constitution de pôles d’activité, introduction de la tarification à l’activité…) et une nouvelle répartition des pouvoirs à l’hôpital (renforcement du poids des managers). De nouveaux modes de management ont été transposés du secteur privé avec pour objectif de perfectionner et de moderniser l’action du secteur public. Cependant, les objectifs de ces deux secteurs ne sont pas les mêmes : satisfaction de l’intérêt général pour l’un et rentabilité pour l’autre. Cette différence peut être enrichissante (exemples : émulation liée à la compétition, réalisation d’économies substantielles…), mais aussi source de résistances et de stress. Cette nouvelle gestion publique déstabilise les différents acteurs des établissements, qui doivent, à la fois, répondre aux grands principes du service public et à des logiques économiques de performance. Dans un tel contexte, des tensions de rôle peuvent-elles se développer chez le personnel soignant ? Par quels moyens serait-il possible de gérer ces tensions de rôle ? Ce travail de recherche s’intéresse tout particulièrement aux différentes stratégies d’ajustement utilisées par les cadres de santé, pour faire face aux tensions qui pourraient se développer. Pour répondre à ces questions, une étude qualitative a été réalisée auprès de cadres de santé d’un CHRU français. Les résultats révèlent que les changements vécus suite à la mise en place du Nouveau Management Public dans les hôpitaux ont modifié le rôle et les fonctions des cadres de santé. Désormais à l’interface entre une culture du soin et une culture gestionnaire, les cadres de santé se trouvent dans une position délicate qui suscite des tensions de rôle quotidiennes. Des stratégies d’ajustement utilisées par les cadres de santé pour faire face à ces tensions de rôle ont également été identifiées

Identification of organizational socialization tactics : The case of sales and marketing trainees in higher education

International audienceThe fast track to employment and the primary road to hiring, learning or traineeship (taken to mean a system of learning or traineeship that alternates periods of theoretical training at the University with practical training in the company) is continuing to grow. Despite its development and its implications for the company (pre-recruitment and investment), few researchers are interested in the socialization of trainees and, in particular, sales and marketing people. The objective of this exploratory study is to identify the organizational practices of socialization put in place for the Customer Advisor trainee employees in the banking/insurance sector, an atypical segment of sales and marketing resources. The results of a qualitative study conducted on the basis of two data collections (33 individual semi-directive interviews were carried out with different actors along with a group interview of 13 professional tutors) reveal particularities related to the socialization of commercial trainees such as the establishment of an organizational context conducive to learning and the crucial role of the tutor. These results also show that the presence of trainees develops role innovation in that which concerns both the trainees and the tutors

Bayesian Regression and Classification Using Gaussian Process Priors Indexed by Probability Density Functions

International audienceIn this paper, we introduce the notion of Gaussian processes indexed by probability density functions for extending the Matérn family of covariance functions. We use some tools from information geometry to improve the efficiency and the computational aspects of the Bayesian learning model. We particularly show how a Bayesian inference with a Gaussian process prior (covariance parameters estimation and prediction) can be put into action on the space of probability density functions. Our framework has the capacity of classifiying and infering on data observations that lie on nonlinear subspaces. Extensive experiments on multiple synthetic, semi-synthetic and real data demonstrate the effectiveness and the efficiency of the proposed methods in comparison with current state-of-the-art methods