Comparison of trend detection methods in GEV models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F20%3APU137328" target="_blank" >RIV/00216305:26210/20:PU137328 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/03610918.2020.1804580?scroll=top&needAccess=true" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/03610918.2020.1804580?scroll=top&needAccess=true</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/03610918.2020.1804580" target="_blank" >10.1080/03610918.2020.1804580</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of trend detection methods in GEV models

Popis výsledku v původním jazyce

In recent environmental studies, the examination of extreme events has great impact. The block maxima of environment-related indices can be analyzed by the tools of extreme value theory. For instance, the monthly maxima of the fire weather index at stations in British Columbia might be modeled by GEV distribution, but it is questionable whether the underlying stochastic process is stationary. This property can lead us to different approaches to determine whether there is a significant trend in the past few years’ data or not. One such approach is a likelihood ratio based procedure, which has favorable asymptotic properties, but for realistic sample sizes it might have large decision errors. In this paper, we analyze the properties of the likelihood ratio test for extremes by bootstrap simulations and present a simulation-based procedure to overcome the problem of small sample sizes. We also propose a return level calculation method. Using our theoretical results we reassess the trends of fire weather index monthly maxima in selected stations of British Columbia.

Název v anglickém jazyce

Comparison of trend detection methods in GEV models

Popis výsledku anglicky

In recent environmental studies, the examination of extreme events has great impact. The block maxima of environment-related indices can be analyzed by the tools of extreme value theory. For instance, the monthly maxima of the fire weather index at stations in British Columbia might be modeled by GEV distribution, but it is questionable whether the underlying stochastic process is stationary. This property can lead us to different approaches to determine whether there is a significant trend in the past few years’ data or not. One such approach is a likelihood ratio based procedure, which has favorable asymptotic properties, but for realistic sample sizes it might have large decision errors. In this paper, we analyze the properties of the likelihood ratio test for extremes by bootstrap simulations and present a simulation-based procedure to overcome the problem of small sample sizes. We also propose a return level calculation method. Using our theoretical results we reassess the trends of fire weather index monthly maxima in selected stations of British Columbia.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Communications in Statistics Part B: Simulation and Computation

ISSN

0361-0918

e-ISSN

1532-4141

Svazek periodika

-

Číslo periodika v rámci svazku

-

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

000572788600001

EID výsledku v databázi Scopus

2-s2.0-85091216178