Section: Overall Objectives
Model selection in Statistics
The research domain for the select project is statistics. Statistical methodology has made great progress over the past few decades, with a variety of statistical learning software packages that support many different methods and algorithms. Users now face the problem of choosing among them, to select the most appropriate method for their data sets and objectives. The problem of model selection is an important but difficult problem both theoretically and practically. Classical model selection criteria, which use penalized minimum-contrast criteria with fixed penalties, are often based on unrealistic assumptions.
select aims to provide efficient model selection criteria with data-driven penalty terms. In this context, select expects to improve the toolkit of statistical model selection criteria from both theoretical and practical perspectives. Currently, select is focusing its effort on variable selection in statistical learning, non-linear regression models with random effects, hidden-structure models and supervised classification. Its domains of application concern reliability, curves classification, phylogeny analysis and classification in genetics. New developments of select activities are concerned with applications in biostatistics (statistical analysis of fMRI data, population pharmacology) and population genetics.