Weighted least squares estimates in partly linear regression models

Anton Schick

Statistics & Probability Letters, 1996, vol. 27, issue 3, 281-287

Abstract: This paper constructs root-n consistent weighted least squares estimates with random weights of the finite-dimensional parameter in the partly linear regression model with heteroscedastic errors. These new estimates have smaller asymptotic dispersion than the least squares type estimates previously constructed in these models.

Keywords: Weighted; least; squares; spline; estimates; Heteroscedasticity (search for similar items in EconPapers)
Date: 1996
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(95)00086-0
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:27:y:1996:i:3:p:281-287

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().