activity
20082019
most citedLikelihood for generally coarsened observations from multi-state or counting process models

20 citations · 21 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ME2019

The revisited knockoffs method for variable selection in L1-penalised regressions

Anne Gégout-Petit, Aurélie Gueudin-Muller, Clémence Karmann

We consider the problem of variable selection in regression models. In particular, we are interested in selecting explanatory covariates linked with the response variable and we wa…

stat.AP2018

A new centered spatio-temporal autologistic regression model. Application to spatio-temporal analysis of esca disease in a vineyard

Anne Gégout-Petit, Lucia Guérin-Dubrana, Shuxian Li

We propose a new centered autologistic spatio-temporal model for binary data on a lattice. The centering allows the interpretation of the autoregression coefficients in separating…

stat.AP2018

Penalized polytomous ordinal logistic regression using cumulative logits. Application to network inference of zero-inflated variables

Aurélie Deveau, Anne Gégout-Petit, Clémence Karmann

We consider the problem of variable selection when the response is ordinal, that is an ordered categorical variable. In particular, we are interested in selecting quantitative expl…

stat.AP20121 cited

Hidden Markov Model for the detection of a degraded operating mode of optronic equipment

Camille Baysse, Didier Bihannic, Anne Gégout-Petit +2

As part of optimizing the reliability, Thales Optronics now includes systems that examine the state of its equipment. The aim of this paper is to use hidden Markov Model to detect…

math.ST200820 cited

Likelihood for generally coarsened observations from multi-state or counting process models

Daniel Commenges, Anne Gégout-Petit

We consider first the mixed discrete-continuous scheme of observation in multistate models; this is a classical pattern in epidemiology because very often clinical status is assess…