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Gaussian Mixture Regression model with logistic weights, a penalized maximum ...
We wish to estimate conditional density using Gaussian Mixture Regression model with logistic weights and means depending on the covariate. We aim at selecting the... -
Pivotal estimation in high-dimensional regression via linear programming
We propose a new method of estimation in high-dimensional linear regression model. It allows for very weak distributional assumptions including heteroscedasticity, and... -
Adaptive estimation of the intensity of some point processes by model selection.
In this thesis, we want to adapt model selection technics to<br />the problem of estimating the intensity of point processes. More precisely, we<br />want... -
HMM Framework, for Industrial Maintenance Activities
This paper uses the Hidden Markov Model to model an industrial process seen as a discrete event system. Different graphical structures based on Markov automata, called... -
Selection of Clusters Number and Features Subset during a Two-Levels Clusteri...
International audience -
Selection Criteria in Regime Switching Conditional Volatility Models
A large number of non linear conditional heteroskedastic models have been proposed in the literature and practitioners do not have always the tools to choose the... -
Model selection for the ℓ2-SVM by following the regularization
International audience -
Estimation de fonctions géométriques et déconvolution
The presented work is divided into three parts. At first, we show that the formalism of model selection allows to bound the decay rate of the estimation error of a... -
Variational Variable Selection To Assess Experimental Condition Relevance In ...
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Adaptive estimation with warped or incomplete data. Application to survival a...
This thesis presents various problems of adaptive functional estimation, using projection and kernel methods, and criterions inspired both by model selection and... -
Adaptive estimation for Hawkes processes; application to genome analysis
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Adaptive tests of homogeneity for a Poisson process
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Analysing grouping of nucleotides in DNA sequences using lumped processes con...
The most commonly used models for analysing local dependencies in DNA sequences are (high-order) Markov chains. Incorporating knowledge relative to the possible... -
Model Selection for Gaussian Latent Block Clustering with the Integrated Clas...
International audience -
An Oracle Approach for Interaction Neighborhood Estimation in Random Field
International audience -
Model selection for Poisson processes with covariates
International audience -
Estimation of the transition density of a Markov chain
International audience -
Statistical Approaches for Segmentation : Application to Genome Annotation
We propose to model the output of transcriptome sequencing technologies (RNA-Seq) using the negative binomial distribution, as well as build segmentation models suited...
