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June, 1992 Efficient Estimates in Semiparametric Additive Regression Models with Unknown Error Distribution
Jack Cuzick
Ann. Statist. 20(2): 1129-1136 (June, 1992). DOI: 10.1214/aos/1176348675

Abstract

Several authors have shown how to efficiently estimate $\beta$ in the semiparametric additive model $y = x'\beta + g(t) + \text{error}$, $g(t)$ smooth but unknown when the error distribution is normal. However, the general theory suggests that efficient estimation should be possible for general error distributions with finite Fisher information even when the error distribution is unknown. In this note we construct a sequence of estimators which achieves this goal under technical assumptions.

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Jack Cuzick. "Efficient Estimates in Semiparametric Additive Regression Models with Unknown Error Distribution." Ann. Statist. 20 (2) 1129 - 1136, June, 1992. https://doi.org/10.1214/aos/1176348675

Information

Published: June, 1992
First available in Project Euclid: 12 April 2007

zbMATH: 0746.62037
MathSciNet: MR1165611
Digital Object Identifier: 10.1214/aos/1176348675

Subjects:
Primary: 62G05
Secondary: 62F35, 62J05

Rights: Copyright © 1992 Institute of Mathematical Statistics

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Vol.20 • No. 2 • June, 1992
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