| Summary: | This thesis is mainly concerned with the estimation of conditional probabilities in two-way contingency
tables, 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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