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September, 1973 Robust Regression: Asymptotics, Conjectures and Monte Carlo
Peter J. Huber
Ann. Statist. 1(5): 799-821 (September, 1973). DOI: 10.1214/aos/1176342503

Abstract

Maximum likelihood type robust estimates of regression are defined and their asymptotic properties are investigated both theoretically and empirically. Perhaps the most important new feature is that the number $p$ of parameters is allowed to increase with the number $n$ of observations. The initial terms of a formal power series expansion (essentially in powers of $p/n$) show an excellent agreement with Monte Carlo results, in most cases down to 4 observations per parameter.

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Peter J. Huber. "Robust Regression: Asymptotics, Conjectures and Monte Carlo." Ann. Statist. 1 (5) 799 - 821, September, 1973. https://doi.org/10.1214/aos/1176342503

Information

Published: September, 1973
First available in Project Euclid: 12 April 2007

zbMATH: 0289.62033
MathSciNet: MR356373
Digital Object Identifier: 10.1214/aos/1176342503

Rights: Copyright © 1973 Institute of Mathematical Statistics

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Vol.1 • No. 5 • September, 1973
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