Two-Step Estimation in a Heteroscedastic Linear Regression Model

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Abstract

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.

Original languageEnglish
Pages (from-to)206-217
Number of pages12
JournalJournal of Mathematical Sciences (United States)
Volume231
Issue number2
DOIs
Publication statusPublished - 1 May 2018

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