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On the asymptotic normality of the R-estimators of the slope parameters of simple linear regression models with associated errors

By Sana Louhichi, Ryozo Miura and Dalibor Volny

Abstract

International audienceThe purpose of this paper is to prove, under mild conditions, the asymptotic normality of the rank estimator of the slope parameter of a simple linear regression model with stationary associated errors. This result follows from a uniform linearity property for linear rank statistics that we establish under general conditions on the dependence of the errors. We prove also a tightness criterion for weighted empirical process constructed from associated triangular arrays. This criterion is needed for the proofs which are based on that of Koul [Behavior of robust estimators in the regression model with dependent errors. Ann Stat. 1977;5(4):681–699] and of Louhichi [Louhichi S. Weak convergence for empirical processes of associated sequences. Ann Inst Henri Poincaré Probabilités Statist. 2000;36(5):547–567]

Topics: Robust estimators, r-estimators, linear models, rank statistics, dependent errors, association, mixing, asymptotic normality, weighted empirical processes, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
Publisher: 'Informa UK Limited'
Year: 2017
DOI identifier: 10.1080/02331888.2016.1261912
OAI identifier: oai:HAL:hal-01892322v1
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