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October 2005 Penalized log-likelihood estimation for partly linear transformation models with current status data
Shuangge Ma, Michael R. Kosorok
Ann. Statist. 33(5): 2256-2290 (October 2005). DOI: 10.1214/009053605000000444

Abstract

We consider partly linear transformation models applied to current status data. The unknown quantities are the transformation function, a linear regression parameter and a nonparametric regression effect. It is shown that the penalized MLE for the regression parameter is asymptotically normal and efficient and converges at the parametric rate, although the penalized MLE for the transformation function and nonparametric regression effect are only n1/3 consistent. Inference for the regression parameter based on a block jackknife is investigated. We also study computational issues and demonstrate the proposed methodology with a simulation study. The transformation models and partly linear regression terms, coupled with new estimation and inference techniques, provide flexible alternatives to the Cox model for current status data analysis.

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Shuangge Ma. Michael R. Kosorok. "Penalized log-likelihood estimation for partly linear transformation models with current status data." Ann. Statist. 33 (5) 2256 - 2290, October 2005. https://doi.org/10.1214/009053605000000444

Information

Published: October 2005
First available in Project Euclid: 25 November 2005

zbMATH: 1086.62056
MathSciNet: MR2211086
Digital Object Identifier: 10.1214/009053605000000444

Subjects:
Primary: 60F05 , 62G08
Secondary: 62B10 , 62G20

Keywords: Current status data , Empirical processes , Nonparametric regression , Semiparametric efficiency , splines , Transformation models

Rights: Copyright © 2005 Institute of Mathematical Statistics

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Vol.33 • No. 5 • October 2005
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