The Annals of Statistics

Penalized quasi-likelihood estimation in partial linear models

Enno Mammen and Sara van de Geer

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Abstract

Consider a partial linear model, where the expectation of a random variable Y depends on covariates $(x, z)$ through $F(\theta_0 x + m_0(z))$, with $\theta_0$ an unknown parameter, and $m_0$ an unknown function. We apply the theory of empirical processes to derive the asymptotic properties of the penalized quasi-likelihood estimator.

Article information

Source
Ann. Statist., Volume 25, Number 3 (1997), 1014-1035.

Dates
First available in Project Euclid: 20 November 2003

Permanent link to this document
https://projecteuclid.org/euclid.aos/1069362736

Digital Object Identifier
doi:10.1214/aos/1069362736

Mathematical Reviews number (MathSciNet)
MR1447739

Zentralblatt MATH identifier
0906.62033

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

Keywords
Asymptotic normality penalized-likelihood rates of convergence

Citation

Mammen, Enno; van de Geer, Sara. Penalized quasi-likelihood estimation in partial linear models. Ann. Statist. 25 (1997), no. 3, 1014--1035. doi:10.1214/aos/1069362736. https://projecteuclid.org/euclid.aos/1069362736


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