The Annals of Statistics

Behavior of Robust Estimators in the Regression Model with Dependent Errors

Hira L. Koul

Full-text: Open access

Abstract

This paper proves the asymptotic linearity in the regression parameter of a class of linear rank statistics when errors in the regression model are strictly stationary and strongly mixing. Besides this, several other weak convergence results are proved which yield the asymptotic normality of $L$ and $M$ estimators of the regression parameter under the above dependent structure. All these results are useful in studying the effect of the above dependence on the asymptotic behavior of $R, M$ and $L$ estimators vis-a-vis the least squares estimator. An example of linear model with Gaussian errors is given where it is shown that the asymptotic efficiency of certain classes of $R, M$ and $L$ estimators relative to the least squared estimator is greater than or equal to its value under the usual independent errors model.

Article information

Source
Ann. Statist., Volume 5, Number 4 (1977), 681-699.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176343892

Digital Object Identifier
doi:10.1214/aos/1176343892

Mathematical Reviews number (MathSciNet)
MR443213

Zentralblatt MATH identifier
0358.62032

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62G35: Robustness

Keywords
Regression parameter $R, M, L$ estimators strongly mixing robustness

Citation

Koul, Hira L. Behavior of Robust Estimators in the Regression Model with Dependent Errors. Ann. Statist. 5 (1977), no. 4, 681--699. doi:10.1214/aos/1176343892. https://projecteuclid.org/euclid.aos/1176343892


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