Two-Step Estimation in a Heteroscedastic Linear Regression Model

Two-Step Estimation in a Heteroscedastic Linear Regression Model Full article

Journal

Journal of Mathematical Sciences (United States)
ISSN: 1072-3374 , E-ISSN: 1573-8795

Output data

Year: 2018, Volume: 231, Number: 2, Pages: 206-217 Pages count : 12 DOI: 10.1007/s10958-018-3816-y

Authors

Linke Y.Y. ^1,2

Affiliations

1	Sobolev Institute of Mathematics SB RAS, 4, Akad. Koptyuga pr., Novosibirsk, 630090, Russian Federation
2	Novosibirsk State University, 1, Pirogova St., Novosibirsk, 630090, Russian Federation

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. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Linke Y.Y.
Two-Step Estimation in a Heteroscedastic Linear Regression Model
Journal of Mathematical Sciences (United States). 2018. V.231. N2. P.206-217. DOI: 10.1007/s10958-018-3816-y Scopus OpenAlex

Scopus:	2-s2.0-85045947229
OpenAlex:	W2799323789

Пока нет цитирований