Economics at your fingertips  

Weak invariance principles for sums of dependent random functions

István Berkes, Lajos Horvath and Gregory Rice

Stochastic Processes and their Applications, 2013, vol. 123, issue 2, 385-403

Abstract: Motivated by problems in functional data analysis, in this paper we prove the weak convergence of normalized partial sums of dependent random functions exhibiting a Bernoulli shift structure.

Keywords: Variables in Hilbert spaces; m–approximability; Weak convergence (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10) 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

Stochastic Processes and their Applications is currently edited by T. Mikosch

More articles in Stochastic Processes and their Applications from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-08-26
Handle: RePEc:eee:spapps:v:123:y:2013:i:2:p:385-403