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2013 Penalized profiled semiparametric estimating functions
Lan Wang, Bo Kai, Cédric Heuchenne, Chih-Ling Tsai
Electron. J. Statist. 7: 2656-2682 (2013). DOI: 10.1214/13-EJS859

Abstract

In this paper, we propose a general class of penalized profiled semiparametric estimating functions which is applicable to a wide range of statistical models, including quantile regression, survival analysis, and missing data, among others. It is noteworthy that the estimating function can be non-smooth in the parametric and/or nonparametric components. Without imposing a specific functional structure on the nonparametric component or assuming a conditional distribution of the response variable for the given covariates, we establish a unified theory which demonstrates that the resulting estimator for the parametric component possesses the oracle property. Monte Carlo studies indicate that the proposed estimator performs well. An empirical example is also presented to illustrate the usefulness of the new method.

Citation

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Lan Wang. Bo Kai. Cédric Heuchenne. Chih-Ling Tsai. "Penalized profiled semiparametric estimating functions." Electron. J. Statist. 7 2656 - 2682, 2013. https://doi.org/10.1214/13-EJS859

Information

Published: 2013
First available in Project Euclid: 30 October 2013

zbMATH: 1274.62261
MathSciNet: MR3138833
Digital Object Identifier: 10.1214/13-EJS859

Subjects:
Primary: 62G05, 62G08
Secondary: 62G20

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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