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Asymptotics of R-, MD- and LAD-estimators in linear regression models with long range dependent errors.

Research output: Contribution to journalJournal article

Published

<mark>Journal publication date</mark>12/1993
<mark>Journal</mark>Probability Theory and Related Fields
Issue4
Volume95
Number of pages19
Pages535-553
<mark>Original language</mark>English

Abstract

This paper establishes the uniform closeness of a weighted residual empirical process to its natural estimate in the linear regression setting when the errors are Gaussian, or a function of Gaussian random variables, that are strictly stationary and long range dependent. This result is used to yield the asymptotic uniform linearity of a class of rank statistics in linear regression models with long range dependent errors. The latter result, in turn, yields the asymptotic distribution of the Jaeckel (1972) rank estimators. The paper also studies the least absolute deviation and a class of certain minimum distance estimators of regression parameters and the kernel type density estimators of the marginal error density when the errors are long range dependent.