Asymptotic and bootstrap tests for a change in autoregression omitting variability estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10383236" target="_blank" >RIV/00216208:11320/18:10383236 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/18:00484127

Výsledek na webu

<a href="http://link-springer-com-443.webvpn.jxutcm.edu.cn/chapter/10.1007%2F978-3-319-96944-2_13" target="_blank" >http://link-springer-com-443.webvpn.jxutcm.edu.cn/chapter/10.1007%2F978-3-319-96944-2_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-96944-2_13" target="_blank" >10.1007/978-3-319-96944-2_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Asymptotic and bootstrap tests for a change in autoregression omitting variability estimation

Popis výsledku v původním jazyce

A sequence of time-ordered observations follows an autoregressive model of order one and its parameter is possibly subject to change at most once at some unknown time point. The aim is to test whether such an unknown change has occurred or not. A change-point method presented here rely on a ratio type test statistic based on the maxima of cumulative sums. The main advantage of the developed approach is that the variance of the observations neither has to be known nor estimated. Asymptotic distribution of the test statistic under the no-change null hypothesis is derived. Moreover, we prove the consistency of the test under the alternative. A bootstrap procedure is proposed in the way of a completely data-driven technique without any tuning parameters. The results are illustrated through a simulation study, which demonstrates the computational efficiency of the procedure. A practical application to real data is presented as well.

Název v anglickém jazyce

Asymptotic and bootstrap tests for a change in autoregression omitting variability estimation

Popis výsledku anglicky

A sequence of time-ordered observations follows an autoregressive model of order one and its parameter is possibly subject to change at most once at some unknown time point. The aim is to test whether such an unknown change has occurred or not. A change-point method presented here rely on a ratio type test statistic based on the maxima of cumulative sums. The main advantage of the developed approach is that the variance of the observations neither has to be known nor estimated. Asymptotic distribution of the test statistic under the no-change null hypothesis is derived. Moreover, we prove the consistency of the test under the alternative. A bootstrap procedure is proposed in the way of a completely data-driven technique without any tuning parameters. The results are illustrated through a simulation study, which demonstrates the computational efficiency of the procedure. A practical application to real data is presented as well.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GJ18-01781Y" target="_blank" >GJ18-01781Y: Dynamické a granulární rezervování škod s využitím kopulí - DaGLoRCo</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Time Series Analysis and Forecasting

ISBN

978-3-319-96943-5

ISSN

1431-1968

e-ISSN

—

Počet stran výsledku

16

Strana od-do

187-202

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

University of Granada, Spain

Datum konání akce

18. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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