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Type de document Thèse/Dissertation Auteur Geenens, Gery Adresse e-mail de l'auteur gery.geenens@uclouvain.be URN BelnUcetd-06252008-101933 Langue Anglais/English Titre Non- and semiparametric models for conditional probabilities in two-way contingency tables / Modèles non-paramétriques et semiparamétriques pour les probabilités conditionnelles dans les tables de contingence à deux entrées Intitulé du diplôme STAT 3 - Doctorat en sciences (statistique) Département/Domaine EUEN/STAT - Institut de statistique Adresse e-mail du département issec@stat.ucl.ac.be Jury
Nom Titre Delecroix, Michel Membre du jury/Committee Member Gijbels, Irène Membre du jury/Committee Member Van Keilegom, Ingrid Membre du jury/Committee Member Denuit, Michel Président du jury/Committee Chair Simar, Léopold Promoteur/Director Mots-clés
- Chi-square test
- Conditional independence
- Likelihood-ratio test
- Régression non-paramétrique
- Modèle à direction révélatrice unique
- Régression semiparamétrique
- Nonparametric regression
- Indépendence conditionnelle
- Test du rapport de vraisemblance
- Test du chi-carré
- Semiparametric regression
- Single-index model
Date de défense 2008-07-04 Type d'accès unrestricted Résumé This thesis is mainly concerned with the estimation of conditional probabilities in two-way contingencytables, that is probabilities of type P(R=i,S=j|X=x), for (i,j) in {1, . . . , r}×{1, . . . , s}, where
R and S are the two categorical variables forming the contingency table, with r and s levels respectively, and
X is a vector of explanatory variables possibly associated with R, S, or both. Analyzing such a conditional
distribution is often of interest, as this allows to go further than the usual unconditional study of the behavior
of the variables R and S. First, one can check an eventual effect of these covariates on the distribution of
the individuals through the cells of the table, and second, one can carry out usual analyses of contingency
tables, such as independence tests, taking into account, and removing in some sense, this effect. This helps
for instance to identify the external factors which could be responsible for an eventual association between
R and S. This also gives the possibility to adapt for a possible heterogeneity in the population of interest,
when analyzing the table.
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