Geodesic
Two-step estimation in heteroscedastic linear regression model
Sibirskij žurnal čistoj i prikladnoj matematiki, Tome 17 (2017) no. 2, pp. 39-51

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We study the problem of estimating a parameter in some linear heteroscedastic 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 estimates.
Keywords: linear regression, order statistics, heteroscedastic, asymptotic normality, $\varphi$-mixing, two-step estimators. 
@article{VNGU_2017_17_2_a3,
     author = {Yu. Yu. Linke},
     title = {Two-step estimation in heteroscedastic linear regression model},
     journal = {Sibirskij \v{z}urnal \v{c}istoj i prikladnoj matematiki},
     pages = {39--51},
     publisher = {mathdoc},
     volume = {17},
     number = {2},
     year = {2017},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VNGU_2017_17_2_a3/}
}
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Yu. Yu. Linke. Two-step estimation in heteroscedastic linear regression model. Sibirskij žurnal čistoj i prikladnoj matematiki, Tome 17 (2017) no. 2, pp. 39-51. http://geodesic.mathdoc.fr/item/VNGU_2017_17_2_a3/