We develop an approach for microarray differential expression analysis, i.e. identifying genes whose expression levels differ between two or more groups. Current approaches to inference rely either on full parametric assumptions or on permutation-based techniques for sampling under the null distribution. In some situations, however, a full parametric model cannot be justified, or the sample siz...

Investigating the information of the French National Soil Tests database for soil monitoring produces multiple hypothesis testing problems with hundreds or thousands of test responses to consider simultaneously. A largely used concept of error control in such multiple testing is the expected proportion of falsely rejected hypotheses, or False Discovery Rate (FDR). A related notion of local FDR ...

____________________________________________________________ 13 Résumé Analytique ________________________________________________________ 14 Chapter 1: Introduction ____________________________________________________ 18 1.1 Background and Motivation ______________________________________ 19 1.2 Objectives ____________________________________________________ 22 1.3 Outlines ___________________...

This paper presents a novel approach to estimation of the cumulative incidence function in the presence of competing risks. The underlying statistical model is specified via a mixture factorization of the joint distribution of the event type and the time to the event. The time to event distributions conditional on the event type are modeled using smooth semi-nonparametric densities. One strengt...

The accurate assessment of participants’ private information may critically affect policy recommendations in auction markets. In many auction environments estimation of the private information distribution may be complicated by the presence of unobserved heterogeneity. This problem arises when some of the information available to all bidders at the time of the auction is subsequently not observ...

We give a general framework of statistical aspects of the problem of understanding and a description of image symmetries, by utilizing the theory of moment invariants. In particular, we examine the issues of joint symmetry estimation and detection. These questions are formulated as the statistical decision and estimation problems since we cope with images observed in the presence of noise. The ...

We propose a novel approach to online estimation of probability density functions, which is based on kernel density estimation (KDE). The method maintains and updates a non-parametric model of the observed data, from which the KDE can be calculated. We propose an online bandwidth estimation approach and a compression/revitalization scheme which maintains the KDE’s complexity low. We compare the...

In this paper, we propose a simple linear least squares framework to deal with estimation and selection for a groupwise additive multiple-index model, of which the partially linear single-index model is a special case, and in which each component function has a single-index structure. We show that, somewhat unexpectedly, all index vectors can be recovered through a single least squares coeffici...