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Section: New Results
Auto-associative models
Serge Iovleff gave a complete treatment of the Auto-Associative models in the semi-linear case and wrote a software for estimating these models (hal-00734070, version 1).
Overall Objectives
Research Program
Application Domains
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
Partnerships and Cooperations
Bibliography