Nonparametric inference about increasing odds rate distributions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F24%3A10253765" target="_blank" >RIV/61989100:27510/24:10253765 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/10485252.2023.2220050" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/10485252.2023.2220050</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/10485252.2023.2220050" target="_blank" >10.1080/10485252.2023.2220050</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Nonparametric inference about increasing odds rate distributions

Popis výsledku v původním jazyce

To improve nonparametric estimates of lifetime distributions, we propose using the increasing odds rate (IOR) model as an alternative to other popular, but more restrictive, &apos;adverse ageing&apos; models, such as the increasing hazard rate one. This extends the scope of applicability of some methods for statistical inference under order restrictions, since the IOR model is compatible with heavy-tailed and bathtub distributions. We study a strongly uniformly consistent estimator of the cumulative distribution function of interest under the IOR constraint. Numerical evidence shows that this estimator often outperforms the classic empirical distribution function when the underlying model does belong to the IOR family. We also study two different tests to detect deviations from the IOR property and establish their consistency. The performance of these tests is also evaluated through simulations.

Název v anglickém jazyce

Nonparametric inference about increasing odds rate distributions

Popis výsledku anglicky

To improve nonparametric estimates of lifetime distributions, we propose using the increasing odds rate (IOR) model as an alternative to other popular, but more restrictive, &apos;adverse ageing&apos; models, such as the increasing hazard rate one. This extends the scope of applicability of some methods for statistical inference under order restrictions, since the IOR model is compatible with heavy-tailed and bathtub distributions. We study a strongly uniformly consistent estimator of the cumulative distribution function of interest under the IOR constraint. Numerical evidence shows that this estimator often outperforms the classic empirical distribution function when the underlying model does belong to the IOR family. We also study two different tests to detect deviations from the IOR property and establish their consistency. The performance of these tests is also evaluated through simulations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/GA20-16764S" target="_blank" >GA20-16764S: Zobecněný přístup ke stochastické dominanci: teorie a finanční aplikace</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Nonparametric Statistics

ISSN

1048-5252

e-ISSN

1029-0311

Svazek periodika

36

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

435-454

Kód UT WoS článku

001002158400001

EID výsledku v databázi Scopus

2-s2.0-85160963266