Economics at your fingertips  

Kernel-based functional principal components

Graciela Boente and Ricardo Fraiman

Statistics & Probability Letters, 2000, vol. 48, issue 4, 335-345

Abstract: In this paper, we propose kernel-based smooth estimates of the functional principal components when data are continuous trajectories of stochastic processes. Strong consistency and the asymptotic distribution are derived under mild conditions.

Keywords: Functional; principal; components; Kernel; methods; Hilbert-Schmidt; operators; Eigenfunctions (search for similar items in EconPapers)
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (18) Track citations by RSS feed

Downloads: (external link)
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:

Ordering information: This journal article can be ordered from
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
Series data maintained by Dana Niculescu ().

Page updated 2017-09-29
Handle: RePEc:eee:stapro:v:48:y:2000:i:4:p:335-345