TY - JOUR
T1 - Model-free variable selection
AU - Li, Lexin
AU - Cook, R. Dennis
AU - Nachtsheim, Christopher J.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Model selection
KW - Sliced inverse regression
KW - Stepwise regression
KW - Sufficient dimension reduction
UR - http://www.scopus.com/inward/record.url?scp=16244422988&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=16244422988&partnerID=8YFLogxK
U2 - 10.1111/j.1467-9868.2005.00502.x
DO - 10.1111/j.1467-9868.2005.00502.x
M3 - Article
AN - SCOPUS:16244422988
SN - 1369-7412
VL - 67
SP - 285
EP - 299
JO - Journal of the Royal Statistical Society. Series B: Statistical Methodology
JF - Journal of the Royal Statistical Society. Series B: Statistical Methodology
IS - 2
ER -