Penalized profiled semiparametric estimating functions

Lan Wang, Bo Kai, Cédric Heuchenne, Chih Ling Tsai

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2656-2682
Number of pages27
JournalElectronic Journal of Statistics
Issue number1
StatePublished - 2013


  • Non-smooth estimating functions
  • Nonconvex penalty
  • Profiled semiparametric estimating functions


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