79 citations · 93 across the 8 of their papers we have counts for
9 papers
Parallel gradient boosting for flexible estimation of conditional distributions
Rémy Chapelle, Nicolas Vayatis, Bruno Falissard +1
Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction problems has found an increasi…
Simultaneous semi-parametric estimation of clustering and regression
Matthieu Marbac, Mohammed Sedki, Christophe Biernacki +1
We investigate the parameter estimation of regression models with fixed group effects, when the group variable is missing while group related variables are available. This problem…
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…
A Family of Blockwise One-Factor Distributions for Modelling High-Dimensional Binary Data
Matthieu Marbac, Mohammed Sedki
We introduce a new family of one factor distributions for high-dimensional binary data. The model provides an explicit probability for each event, thus avoiding the numeric approxi…
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…