8 citations · 14 across the 3 of their papers we have counts for
4 papers
Variable selection in model-based clustering and discriminant analysis with a regularization approach
Gilles Celeux, Cathy Maugis-Rabusseau, Mohammed Sedki
Relevant methods of variable selection have been proposed in model-based clustering and classification. These methods are making use of backward or forward procedures to define the…
Variable selection for mixed data clustering: a model-based approach
Matthieu Marbac, Mohammed Sedki
We propose two approaches for selecting variables in latent class analysis (i.e.,mixture model assuming within component independence), which is the common model-based clustering m…
Bayesian model selection in logistic regression for the detection of adverse drug reactions
Matthieu Marbac, Pascale Tubert-Bitter, Mohammed Sedki
Motivation: Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, exploring such databases requires statist…
Efficient learning in ABC algorithms
Mohammed Sedki, Pierre Pudlo, Jean-Michel Marin +2
Approximate Bayesian Computation has been successfully used in population genetics to bypass the calculation of the likelihood. These methods provide accurate estimates of the post…