Premium
Model‐free variable selection
Author(s) -
Li Lexin,
Dennis Cook R.,
Nachtsheim Christopher J.
Publication year - 2005
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2005.00502.x
Subject(s) - feature selection , computer science , selection (genetic algorithm) , smoothing , model selection , variable (mathematics) , class (philosophy) , nonparametric statistics , dimension (graph theory) , mathematical optimization , machine learning , data mining , artificial intelligence , econometrics , mathematics , mathematical analysis , pure mathematics , computer vision
Summary. The importance of variable selection in regression has grown in recent years as computing power has encouraged the modelling of data sets of ever‐increasing size. Data mining applications in finance, marketing and bioinformatics are obvious examples. A limitation of nearly all existing variable selection methods is the need to specify the correct model before selection. When the number of predictors is large, model formulation and validation can be difficult or even infeasible. On the basis of the theory of sufficient dimension reduction, we propose a new class of model‐free variable selection approaches. The methods proposed assume no model of any form, require no nonparametric smoothing and allow for general predictor effects. The efficacy of the methods proposed is demonstrated via simulation, and an empirical example is given.