StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning"

16 mai 2013 · 1 h 1 min

Bayesian inference for the exponential random graph model (Nial Friel)

The exponential random graph is arguably the most popular model for the statistical analysis of network data. However despite its widespread use, it is very complicated to handle from a statistical perspective,...

16 mai 2013 · 55 min 48 s

Clustering of variables combined with variable selection using random forests : application to gene expression data (Robin Genuer & Vanessa Kuentz-Simonet)

The main goal of this work is to tackle the problem of dimension reduction for highdimensional supervised classification. The motivation is to handle gene expression data. The proposed method works in 2...

16 mai 2013 · 48 min 50 s

Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator (Arnaud Doucet)

When an unbiased estimator of the likelihood is used within an Markov chain Monte Carlo (MCMC) scheme, it is necessary to tradeoff the number of samples used against the computing time. Many...

16 mai 2013 · 54 min 1 s

Investigating on nonlinear relationship in high-dimensional setting (Frédéric Ferraty)

The high dimensional setting is a modern and dynamic research area in Statistics. It covers numerous situations where the number of explanatory variables is much larger than the sample size. This is...

16 mai 2013 · 1 h 6 min

Learning with the Online EM Algorithm (Olivier Cappé)

The Online Expectation-Maximization (EM) is a generic algorithm that can be used to estimate the parameters of latent data models incrementally from large volumes of data. The general principle of the approach...

16 mai 2013 · 49 min 34 s

Modular priors for partially identified models (Ioanna Manolopoulou)

This work is motivated by the challenges of drawing inferences from presence-only data. For example, when trying to determine what habitat sea-turtles "prefer" we only have data on where turtles were observed,...

16 mai 2013 · 58 min 6 s

New challenges for (biological) network inference with sparse Gaussian graphical models (Julien Chiquet)

Network inference methods based upon sparse Gaussian Graphical Models (GGM) have recently emerged as a promising exploratory tool in genomics. They give a sounded representation of direct relationships between genes and are...

16 mai 2013 · 51 min 6 s

Regularized PCA to denoise and visualize data (Julie Josse)

Principal component analysis (PCA) is a well-established method commonly used to explore and visualize data. A classical PCA model is the fixed effect model where data are generated as a fixed structure...

16 mai 2013 · 57 min 42 s

Strategies to analyze (Benoît Liquet)

Recent technological advances in molecular biology have given rise to numerous large scale datasets whose analysis have risen serious methodological challenges mainly relating to the size and complex structure of the data....