We herein introduce a general variable selection procedure, which can be applied to several parametric multivariate problems, including principal components and regression, among others. The aim is to allow the identification of a small subset of the original variables that can ‘better explain’ the model through nonparametric relationships. The method typically yields some noisy uninformative variables and some variables that are strongly related because of their general dependence and our aim is to help understand the underlying structures in a given data–set. The asymptotic behaviour of the proposed method is considered and some real and simulated data–sets are analysed as examples.
Accepté le :
DOI : 10.1051/ps/2016022
Mots clés : Variable selection, regression, principal components analysis
@article{PS_2016__20__463_0,
author = {Fraiman, Ricardo and Gimenez, Yanina and Svarc, Marcela},
title = {Seeking relevant information from a statistical model},
journal = {ESAIM: Probability and Statistics},
pages = {463--479},
publisher = {EDP-Sciences},
volume = {20},
year = {2016},
doi = {10.1051/ps/2016022},
zbl = {1353.62070},
language = {en},
url = {http://www.numdam.org/articles/10.1051/ps/2016022/}
}
TY - JOUR AU - Fraiman, Ricardo AU - Gimenez, Yanina AU - Svarc, Marcela TI - Seeking relevant information from a statistical model JO - ESAIM: Probability and Statistics PY - 2016 SP - 463 EP - 479 VL - 20 PB - EDP-Sciences UR - http://www.numdam.org/articles/10.1051/ps/2016022/ DO - 10.1051/ps/2016022 LA - en ID - PS_2016__20__463_0 ER -
%0 Journal Article %A Fraiman, Ricardo %A Gimenez, Yanina %A Svarc, Marcela %T Seeking relevant information from a statistical model %J ESAIM: Probability and Statistics %D 2016 %P 463-479 %V 20 %I EDP-Sciences %U http://www.numdam.org/articles/10.1051/ps/2016022/ %R 10.1051/ps/2016022 %G en %F PS_2016__20__463_0
Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela. Seeking relevant information from a statistical model. ESAIM: Probability and Statistics, Tome 20 (2016), pp. 463-479. doi : 10.1051/ps/2016022. http://www.numdam.org/articles/10.1051/ps/2016022/
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