Two-Step Estimation in a Heteroscedastic Linear Regression Model


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We study the problem of estimating a parameter in some heteroscedastic linear regression model in the case where the regressors consist of all order statistics based on the sample of identically distributed not necessarily independent observations with finite second moment. It is assumed that the random errors depend on the parameter and distributions of the corresponding regressors. We propose a two-step procedure for finding explicit asymptotically normal estimators.

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Yu. Linke

Sobolev Institute of Mathematics SB RAS; Novosibirsk State University

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Email: linke@math.nsc.ru
俄罗斯联邦, 4, Akad. Koptyuga pr., Novosibirsk, 630090; 1, Pirogova St., Novosibirsk, 630090

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