The Brouwer Lecture 2005: Statistical estimation with model selection
Abstract
The purpose of this paper is to explain the interest and importance of (approximate) models and model selection in Statistics. Starting from the very elementary example of histograms we present a general notion of finite dimensional model for statistical estimation and we explain what type of risk bounds can be expected from the use of one such model. We then give the performance of suitable model selection procedures from a family of such models. We illustrate our point of view by two main examples: the choice of a partition for designing a histogram from an nsample and the problem of variable selection in the context of Gaussian regression.
 Publication:

arXiv Mathematics eprints
 Pub Date:
 May 2006
 arXiv:
 arXiv:math/0605187
 Bibcode:
 2006math......5187B
 Keywords:

 Mathematics  Statistics