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• W2022604426 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." @default.
• W2022604426 created "2016-06-24" @default.
• W2022604426 creator A5012683086 @default.
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• W2022604426 date "2013-01-01" @default.
• W2022604426 modified "2023-09-26" @default.
• W2022604426 title "Penalized profiled semiparametric estimating functions" @default.
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• W2022604426 doi "https://doi.org/10.1214/13-ejs859" @default.
• W2022604426 hasPublicationYear "2013" @default.
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