Convergence rates for trigonometric and polynomial-trigonometric regression estimators

R. L. Eubank and Paul Speckman

Statistics & Probability Letters, 1991, vol. 11, issue 2, 119-124

Abstract: Upper bounds are derived for the rates of convergence for trigonometric series regression estimators of an unknown, smooth regression function. The resulting rates depend on the regression function satisfying certain periodic boundary conditions that may not hold in practice. To overcome such difficulties alternative estimators are proposed which are obtained by regression on trigonometric functions and low-order polynomials. These estimators are shown to always be capable of obtaining the optimal rates of convergence over a particular smoothness class of functions, irregardless of whether or not the regression function is periodic.

Keywords: Guaranteed; rates; mean; squared; error; nonparametric; regression; orthogonal; series (search for similar items in EconPapers)
Date: 1991
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(91)90128-E
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:11:y:1991:i:2:p:119-124

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 ().