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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties (2001)
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| Citations: | 942 - 61 self |
BibTeX
@MISC{Fan01variableselection,
author = {Jianqing Fan and Runze Li},
title = {Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties},
year = {2001}
}
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Abstract
Variable selection is fundamental to high-dimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized likelihood approaches are proposed to handle these kinds of problems. The proposed methods select variables and estimate coefficients simultaneously. Hence they enable us to construct confidence intervals for estimated parameters. The proposed approaches are distinguished from others in that the penalty functions are symmetric, nonconcave on (0, ∞), and have singularities at the origin to produce sparse solutions. Furthermore, the penalty functions should be bounded by a constant to reduce bias and satisfy certain conditions to yield continuous solutions. A new algorithm is proposed for optimizing penalized likelihood functions. The proposed ideas are widely applicable. They are readily applied to a variety of parametric models such as generalized linear models and robust regression models. They can also be applied easily to nonparametric modeling by using wavelets and splines. Rates of convergence of the proposed penalized likelihood estimators are established. Furthermore, with proper choice of regularization parameters, we show that the proposed estimators perform as well as the oracle procedure in variable selection; namely, they work as well as if the correct submodel were known. Our simulation shows that the newly proposed methods compare favorably with other variable selection techniques. Furthermore, the standard error formulas are tested to be accurate enough for practical applications.
Keyphrases
variable selection nonconcave penalized likelihood oracle property penalty function variable selection technique high-dimensional statistical modeling likelihood function oracle procedure likelihood estimator proper choice generalized linear model nonparametric regression parametric model satisfy certain condition method compare regularization parameter sparse solution method select variable practical application estimate coefficient continuous solution variable selection process likelihood approach robust regression model stepwise selection procedure many approach stochastic error standard error formula correct submodel new algorithm confidence interval
