Doctor of Philosophy / Department of Statistics / Wei-Wen Hsu / Evaluating heterogeneity in the class of zero-inflated models has attracted considerable attention in the literature, where the heterogeneity refers to the instances of zero counts generated from two different sources. The mixture probability or the so-called mixing weight in the zero-inflated model is used to measure the extent of such heterogeneity in the population. Typically, the homogeneity tests are employed to examine the mixing weight at zero. Various testing procedures for homogeneity in zero-inflated models, such as score test and Wald test, have been well discussed and established in the literature. However, it is well known that these classical tests require the correct model specification in order to provide valid statistical inferences. In practice, the testing procedure could be performed under model misspecification, which could result in biased and invalid inferences. There are two common misspecifications in zero-inflated models, which are the incorrect specification of the baseline distribution and the misspecified mean function of the baseline distribution. As an empirical evidence, intensive simulation studies revealed that the empirical sizes of the homogeneity tests for zero-inflated models might behave extremely liberal and unstable under these misspecifications for both cross-sectional and correlated count data. We propose a robust score statistic to evaluate heterogeneity in cross-sectional zero-inflated data. Technically, the test is developed based on the Poisson-Gamma mixture model which provides a more general framework to incorporate various baseline distributions without specifying their associated mean function. The testing procedure is further extended to correlated count data. We develop a robust Wald test statistic for correlated count data with the use of working independence model assumption coupled with a sandwich estimator to adjust for any misspecification of the covariance structure in the data. The empirical performances of the proposed robust score test and Wald test are evaluated in simulation studies. It is worth to mention that the proposed Wald test can be implemented easily with minimal programming efforts in a routine statistical software such as SAS. Dental caries data from the Detroit Dental Health Project (DDHP) and Girl Scout data from Scouting Nutrition and Activity Program (SNAP) are used to illustrate the proposed methodologies.

La première partie de la thèse traite d’inférence combinatoire en Analyse Géométrique des Données (AGD). Nous proposons des tests multidimensionnels sans hypothèse sur le processus d’obtention des données ou les distributions. Nous nous intéressons ici aux problèmes de typicalité (comparaison d’un point moyen à un point de référence ou d’un groupe d’observations à une population de référence) et d’homogénéité (comparaison de plusieurs groupes). Nous utilisons des procédures combinatoires pour construire un ensemble de référence par rapport auquel nous situons les données. Les statistiques de test choisies mènent à des prolongements originaux : interprétation géométrique du seuil observé et construction d’une zone de compatibilité.La seconde partie présente l’étude de l’absentéisme dans les Industries Electriques et Gazières de 1995 à 2011 (avec construction d’une cohorte épidémiologique). Des méthodes d’AGD sont utilisées afin d’identifier des pathologies émergentes et des groupes d’agents sensibles. / The first part of this PhD thesis deals with combinatorial inference methods forGeometric Data Analysis (GDA). We propose multidimensional tests that make no assumption on the process of generating data or distributions. We focus particularly on problems of typicality (comparison of a mean point to a reference point or comparison of a group of observations to a reference population) and on problems of homogeneity (comparison of several groups). These methods consist in using combinatorial procedures to build a reference set with respect to which we situate the data. The chosen test statistics lead to original extensions: geometric interpretation of the observed level and construction of a compatibilityzone.The second part of this thesis presents the study of absenteeism in the French Electricity and Gas Industries from 1995 to 2011 (with construction of an epidemiological cohort). GDA methods are used to identify emerging diseases and sensitive groups of agents.