Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models
Year: 2008
Type: article
Abstract: We propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions. Important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors. Unlike the existing penalized maximum likelihood estimators, the proposed penalized estimating functions may not pertain to the derivatives of any objective functions and may be discrete in the regression coefficients. We establish a general asymptotic theory for penalized estimating functions and present suitable numerical algorithms to implement the proposed estimators. In addition, we develop a resampling technique to estimate the variances of the estimated regression coefficients when the asymptotic variances cannot be evaluated directly. Simulation studies demonstrate that the proposed methods perform well in variable selection and variance estimation. We illustrate our methods using data from the Paul Coverdell Stroke Registry.
Authors: Brent A Johnson, D. Y Lin, Donglin Zeng
Institutions: University of North Carolina at Chapel Hill, Emory University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Statistical Methods in Chronic Disease Research, Antiretroviral Treatment Strategies with Optional Switching Times, Semiparametric Regression Analysis of Censored Data, G9000102330, G9000131094, G9000310219, UNC Center for Environmental Health and Susceptibility, Biostatistics for Research in Environmental Health
OpenAlex ID: W2002316706