Learning Out of Leaders
Abstract
This paper investigates the estimation problem in a regression-type model. To be able to deal with potential high dimensions, we provide a procedure called LOL, for Learning Out of Leaders with no optimization step. LOL is an auto-driven algorithm with two thresholding steps. A first adaptive thresholding helps to select leaders among the initial regressors in order to obtain a first reduction of dimensionality. Then a second thresholding is performed on the linear regression upon the leaders. The consistency of the procedure is investigated. Exponential bounds are obtained, leading to minimax and adaptive results for a wide class of sparse parameters, with (quasi) no restriction on the number p of possible regressors. An extensive computational experiment is conducted to emphasize the practical good performances of LOL.
Keywords
Cite
@article{arxiv.1001.1919,
title = {Learning Out of Leaders},
author = {Mathilde Mougeot and Dominique Picard and Karine Tribouley},
journal= {arXiv preprint arXiv:1001.1919},
year = {2011}
}