Ensemble methods aim at improving the predictive performance of a given statistical learning or model fitting technique. The general principleof ensemble methods is to construct a linear combinationof some model fitting methods, instead of using a single fit of the method.

We derive some decision rules to select best predictive regression models in a credibility context, that is, in a 'random effects' linear regression model with replicates. In contrast to usual model selection techniques on a collective level, our proposal allows to detect individual structures,...

Ensemble methods aim at improving the predictive performance of a given statistical learning or model fitting technique. The general principleof ensemble methods is to construct a linear combinationof some model fitting methods, instead of using a single fit of the method.

We present a short selective review of causal inference from observational data, with a particular emphasis on the high-dimensional scenario where the number of measured variables may be much larger than sample size. Despite major identifiability problems, making causal inference from...

Random Forests in combination with Stability Selection allow to estimate stable conditional independence graphs with an error control mechanism for false positive selection. This approach is applicable to graphs containing both continuous and discrete variables at the same time. Its performance...