The Annals of Statistics

Asymptotic Relations of $M$-Estimates and $R$-Estimates in Linear Regression Model

Jana Jureckova

Full-text: Open access

Abstract

Let $\hat{\mathbf{\Delta}}_M$ be an $M$-estimator (maximum-likelihood type estimator) and $\hat{\mathbf{\Delta}}_R$ be an $R$-estimator (rank estimator) of the parameter $\mathbf{\Delta} = (\Delta_1,\cdots, \Delta_p)$ in the linear regression model $X_{Ni} = \sum^p_{j=1} \Delta_jc_{ji} + e_i, i = 1,\cdots, N$. The asymptotic distribution of $\hat\mathbf{\Delta}_M - \hat\mathbf{\Delta}_R$ is derived for $p$ fixed and $N \rightarrow \infty,$ under some assumptions on the design matrix, on the error distribution $F$ and on the functions generating the respective estimators. The result has several consequences which have an interest of their own; among others, it is shown that to any $M$-estimator corresponds an $R$-estimator such that the estimators asymptotically equivalent, and conversely. A special case when $\hat\mathbf{\Delta}_M$ is the maximum likelihood estimator and $\hat\mathbf{\Delta}_R$ the $R$-estimator, both asymptotically efficient for some distribution $G$, is also considered.

Article information

Source
Ann. Statist., Volume 5, Number 3 (1977), 464-472.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176343843

Digital Object Identifier
doi:10.1214/aos/1176343843

Mathematical Reviews number (MathSciNet)
MR433698

Zentralblatt MATH identifier
0365.62034

JSTOR
links.jstor.org

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

Keywords
$M$-estimate $R$-estimate asymptotically normal distribution

Citation

Jureckova, Jana. Asymptotic Relations of $M$-Estimates and $R$-Estimates in Linear Regression Model. Ann. Statist. 5 (1977), no. 3, 464--472. doi:10.1214/aos/1176343843. https://projecteuclid.org/euclid.aos/1176343843


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