Nuisance-parameter-free changepoint detection in non-stationary series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10418295" target="_blank" >RIV/00216208:11320/20:10418295 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=pmLtB2EOrA" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=pmLtB2EOrA</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11749-019-00659-1" target="_blank" >10.1007/s11749-019-00659-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Nuisance-parameter-free changepoint detection in non-stationary series

Popis výsledku v původním jazyce

Many changepoint detection procedures rely on the estimation of nuisance parameters (like long-run variance). If a change has occurred, estimators might be biased and data adaptive rules for the choice of tuning parameters might not work as expected. If the data are not stationary, this becomes more challenging. The aim of this paper is to present two changepoint tests, which involve neither nuisance nor tuning parameters. This is achieved by combing self-normalization and wild bootstrap. We investigate the asymptotic behavior and show the consistency of the bootstrap under the hypothesis as well as under the alternative, assuming mild conditions on the weak dependence of the time series. As a by-product, a changepoint estimator is introduced and its consistency is proved. The results are illustrated through a simulation study. The new completely data-driven tests are applied to real data examples from finance and hydrology.

Název v anglickém jazyce

Nuisance-parameter-free changepoint detection in non-stationary series

Popis výsledku anglicky

Many changepoint detection procedures rely on the estimation of nuisance parameters (like long-run variance). If a change has occurred, estimators might be biased and data adaptive rules for the choice of tuning parameters might not work as expected. If the data are not stationary, this becomes more challenging. The aim of this paper is to present two changepoint tests, which involve neither nuisance nor tuning parameters. This is achieved by combing self-normalization and wild bootstrap. We investigate the asymptotic behavior and show the consistency of the bootstrap under the hypothesis as well as under the alternative, assuming mild conditions on the weak dependence of the time series. As a by-product, a changepoint estimator is introduced and its consistency is proved. The results are illustrated through a simulation study. The new completely data-driven tests are applied to real data examples from finance and hydrology.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2020

Kód důvěrnosti údajů

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

Název periodika

Test

ISSN

1133-0686

e-ISSN

—

Svazek periodika

29

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

ES - Španělské království

Počet stran výsledku

30

Strana od-do

379-408

Kód UT WoS článku

000530843000007

EID výsledku v databázi Scopus

2-s2.0-85065332201