Project-Team:MODAL

Project-Team Modal

Members

Overall Objectives

MOdel for Data Analysis and Learning
Highlights of the Year

Research Program

Generative model design
Data visualization

Application Domains

Domain

Software and Platforms

Rmixmod package for mixed data
RankClust package for rank data
Clere package for high dimensional regression
Clustericat package for correlated categorical variable
CorReg package for correlated variables in regression
AAM
BlockCluster
HDPenReg
STK++ release 0.5
Funclustering package for R
metaMA
metaRNASeq
MPAGenomics
SMVar

New Results

Resampling procedures
Kernel change-point
Gaussian process in RKHS
Model for conditionally correlated categorical data
Mixture model for mixed kind of data
Mixture of Gaussians with Missing Data
Transfert learning in model-based clustering
Gaussian Models Scale Invariant and Stable by Projection
Clustering and variable selection in regression
An AIC-like criterion for semi-supervised classification
Consistency of a nonparametric conditional mode estimator for random fields
Spatial linear models
Auto-associative models
BlockCluster
Rmixmod
Clustering for functional data
Wavelet based clustering using mixed effects functional models
Differential meta-analysis of RNA-seq data from multiple studies
Toxoplasma transcription factor TgAP2XI-5 regulates the expression of genes involved in parasite virulence and host invasion

Bilateral Contracts and Grants with Industry

Arcelor-Mittal
Banque Accord
Hi Duty Free
AGLAE
Alicante

Partnerships and Cooperations

Regional Initiatives
National events
International Research Visitors

Dissemination

Scientific Animation
Teaching - Supervision - Juries
Popularization

Bibliography

Major publications
Publications of the year

Inria | Raweb 2013 | Presentation of the Project-Team MODAL


	PDF	e-Pub

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Section: New Results

Resampling procedures

Participant : Alain Celisse.

The new deep understanding of cross-validation procedures in density estimation has been tackled with new results in terms of risk estimation and model selection [7] . This is the first step towards a fully data-driven and optimal choice of cross-validation strategy.

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