The Annals of Statistics

Penalized maximum likelihood and semiparametric second-order efficiency

A. S. Dalalyan, G. K. Golubev, and A. B. Tsybakov

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Abstract

We consider the problem of estimation of a shift parameter of an unknown symmetric function in Gaussian white noise. We introduce a notion of semiparametric second-order efficiency and propose estimators that are semiparametrically efficient and second-order efficient in our model. These estimators are of a penalized maximum likelihood type with an appropriately chosen penalty. We argue that second-order efficiency is crucial in semiparametric problems since only the second-order terms in asymptotic expansion for the risk account for the behavior of the “nonparametric component” of a semiparametric procedure, and they are not dramatically smaller than the first-order terms.

Article information

Source
Ann. Statist., Volume 34, Number 1 (2006), 169-201.

Dates
First available in Project Euclid: 2 May 2006

Permanent link to this document
https://projecteuclid.org/euclid.aos/1146576260

Digital Object Identifier
doi:10.1214/009053605000000895

Mathematical Reviews number (MathSciNet)
MR2275239

Zentralblatt MATH identifier
1091.62020

Subjects
Primary: 62G05: Estimation 62G20: Asymptotic properties

Keywords
Semiparametric estimation estimating a shift of a nonparametric function second-order efficiency penalized maximum likelihood exact minimax asymptotics

Citation

Dalalyan, A. S.; Golubev, G. K.; Tsybakov, A. B. Penalized maximum likelihood and semiparametric second-order efficiency. Ann. Statist. 34 (2006), no. 1, 169--201. doi:10.1214/009053605000000895. https://projecteuclid.org/euclid.aos/1146576260


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