DEPENDENT FUNCTIONAL LINEAR MODELS WITH APPLICATIONS TO MONITORING STRUCTURAL CHANGE

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10282975" target="_blank" >RIV/00216208:11320/14:10282975 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5705/ss.2012.233" target="_blank" >http://dx.doi.org/10.5705/ss.2012.233</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5705/ss.2012.233" target="_blank" >10.5705/ss.2012.233</a>

Jazyk výsledku

angličtina

Název v původním jazyce

DEPENDENT FUNCTIONAL LINEAR MODELS WITH APPLICATIONS TO MONITORING STRUCTURAL CHANGE

Popis výsledku v původním jazyce

We study sequential monitoring procedures that detect instabilities of the regression operator in an underlying (fully) functional regression model allowing for dependence. These open-end and closed-end procedures are built on a functional principal components analysis of both the predictor and response functions, thus giving rise to multivariate detector functions, whose fluctuations are compared against a curved threshold function. The main theoretical result of the paper quantifies the large-sample behavior of the procedures under the null hypothesis of a stable regression operator. To establish these limit results, classical results on functional principal components analysis are generalized to a dependent setting, which may be of interest in its own sake. In an accompanying empirical study we illustrate the finite sample properties, while an application to environmental data highlights practical usefulness. To the best of our knowledge this is the first paper that combines sequent

Název v anglickém jazyce

DEPENDENT FUNCTIONAL LINEAR MODELS WITH APPLICATIONS TO MONITORING STRUCTURAL CHANGE

Popis výsledku anglicky

We study sequential monitoring procedures that detect instabilities of the regression operator in an underlying (fully) functional regression model allowing for dependence. These open-end and closed-end procedures are built on a functional principal components analysis of both the predictor and response functions, thus giving rise to multivariate detector functions, whose fluctuations are compared against a curved threshold function. The main theoretical result of the paper quantifies the large-sample behavior of the procedures under the null hypothesis of a stable regression operator. To establish these limit results, classical results on functional principal components analysis are generalized to a dependent setting, which may be of interest in its own sake. In an accompanying empirical study we illustrate the finite sample properties, while an application to environmental data highlights practical usefulness. To the best of our knowledge this is the first paper that combines sequent

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP201%2F12%2F1277" target="_blank" >GAP201/12/1277: Statistické modelování trendu pro závislá data</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Statistica Sinica

ISSN

1017-0405

e-ISSN

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Svazek periodika

24

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

TW - Čínská republika (Tchaj-wan)

Počet stran výsledku

31

Strana od-do

1043-1073

Kód UT WoS článku

000340697800001

EID výsledku v databázi Scopus

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