Two-Step Estimation in a Heteroscedastic Linear Regression Model

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 ra...

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Veröffentlicht in:	Journal of mathematical sciences (New York, N.Y.) N.Y.), 2018-05, Vol.231 (2), p.206-217
1. Verfasser:	Linke, Yu. Yu
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Sprache:	eng
Schlagworte:	ASYMPTOTIC SOLUTIONS ERRORS MATHEMATICAL METHODS AND COMPUTING Mathematics Mathematics and Statistics Parameter estimation Random errors RANDOMNESS REGRESSION ANALYSIS Regression models
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description	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.
doi_str_mv	10.1007/s10958-018-3816-y
format	Article
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subjects	ASYMPTOTIC SOLUTIONS ERRORS MATHEMATICAL METHODS AND COMPUTING Mathematics Mathematics and Statistics Parameter estimation Random errors RANDOMNESS REGRESSION ANALYSIS Regression models
title	Two-Step Estimation in a Heteroscedastic Linear Regression Model
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