On combining independent nonparametric regression estimators

Patrick D. Gerard and William R. Schucany

Statistics & Probability Letters, 1996, vol. 26, issue 1, 25-34

Abstract: Three estimators are investigated for linearly combining independent nonparametric regression estimators. Assuming fixed designs, the asymptotic mean squared errors and asymptotically optimal bandwidths are given for each estimator and compared. One estimator essentially ignores the differences in the sources and naively pools all of the data. The second utilizes individually optimized bandwidths and then estimates the best weights to combine them. The third estimator solves a general minimization problem and employs equal bandwidths and weights similar to those for combining unbiased estimators with unequal variance. It is found to be superior to the other two in most situations that would be encountered in practice.

Keywords: Asymptotic; optimality; Bandwidth; Kernel; Local; linear (search for similar items in EconPapers)
Date: 1996
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(94)00248-7
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:26:y:1996:i:1:p:25-34

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 ().