Weak invariance principles for sums of dependent random functions.

István Berkes; Lajos Horvath; Gregory Rice

doi:10.1016/j.spa.2012.10.003

Weak invariance principles for sums of dependent random functions.

István Berkes, Lajos Horvath, Gregory Rice

Institute of Statistics (5060)

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	385-403
Journal	Stochastic Processes and their Applications
Volume	123
Issue number	2
DOIs	https://doi.org/10.1016/j.spa.2012.10.003
Publication status	Published - 2013

Keywords

Variables in Hilbert spaces
m-approximability
Weak convergence

Fields of Expertise

Information, Communication & Computing

Treatment code (Nähere Zuordnung)

Theoretical

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10.1016/j.spa.2012.10.003

1-s2.0-S0304414912002207-mainFinal published version, 231 KBLicence: CC BY 4.0

Cite this

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title = "Weak invariance principles for sums of dependent random functions.",

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",

author = "Istv{\'a}n Berkes and Lajos Horvath and Gregory Rice",

year = "2013",

doi = "10.1016/j.spa.2012.10.003",

language = "English",

volume = "123",

pages = "385--403",

journal = "Stochastic Processes and their Applications ",

issn = "1879-209X",

publisher = "Elsevier B.V.",

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T1 - Weak invariance principles for sums of dependent random functions.

AU - Berkes, István

AU - Horvath, Lajos

AU - Rice, Gregory

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Variables in Hilbert spaces

KW - m-approximability

KW - Weak convergence

UR - http://www.sciencedirect.com/science/journal/03044149/123/2

UR - http://www.sciencedirect.com/science/article/pii/S0304414912002207

U2 - 10.1016/j.spa.2012.10.003

DO - 10.1016/j.spa.2012.10.003

M3 - Article

SN - 1879-209X

VL - 123

SP - 385

EP - 403

JO - Stochastic Processes and their Applications

JF - Stochastic Processes and their Applications

IS - 2

ER -

Weak invariance principles for sums of dependent random functions.

Abstract

Keywords

Fields of Expertise

Treatment code (Nähere Zuordnung)

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Asymptotic and Statistical Analysis of Time Series in Economy and Finance

Cite this

Weak invariance principles for sums of dependent random functions.

Abstract

Keywords

Fields of Expertise

Treatment code (Nähere Zuordnung)

Access to Document

Other files and links

Projects

Asymptotic and Statistical Analysis of Time Series in Economy and Finance

Cite this