Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses

Introduced robust density-based estimators in the context of one-shot devices with exponential lifetimes under a single stress factor. However, it is usual to have several stress factors in industrial experiments involving one-shot devices. In this paper, the weighted minimum density power divergenc...

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Detalhes bibliográficos
Autores: Balakrishnan, Narayanaswamy, Castilla González, Elena María, Martín Apaolaza, Nirian, Pardo Llorente, Leandro
Formato: artículo
Fecha de publicación:2020
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/7223
Acesso em linha:https://hdl.handle.net/20.500.14352/7223
Access Level:acceso abierto
Palavra-chave:519.2
Exponential distribution
Minimum density power divergence estimator
Multiple stresses
One-shot devices
Robustness
Wald-type tests
Estadística matemática (Matemáticas)
Probabilidades (Matemáticas)
1209 Estadística
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oai_identifier_str oai:docta.ucm.es:20.500.14352/7223
network_acronym_str ES
network_name_str España
repository_id_str
spelling Robust inference for one‐shot device testing data under exponential lifetime model with multiple stressesBalakrishnan, NarayanaswamyCastilla González, Elena MaríaMartín Apaolaza, NirianPardo Llorente, Leandro519.2Exponential distributionMinimum density power divergence estimatorMultiple stressesOne-shot devicesRobustnessWald-type testsEstadística matemática (Matemáticas)Probabilidades (Matemáticas)1209 EstadísticaIntroduced robust density-based estimators in the context of one-shot devices with exponential lifetimes under a single stress factor. However, it is usual to have several stress factors in industrial experiments involving one-shot devices. In this paper, the weighted minimum density power divergence estimators (WMDPDEs) are developed as a natural extension of the classical maximum likelihood estimators (MLEs) for one-shot device testing data under exponential lifetime model with multiple stresses. Based on these estimators, Wald-type test statistics are also developed. Through a simulation study, it is shown that some WMDPDEs have a better performance than the MLE in relation to robustness. Two examples with multiple stresses show the usefulness of the model and, in particular, of the proposed estimators, both in engineering and medicine.WileyUniversidad Complutense de Madrid20202020-05-2020202020-05-20journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/7223reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial 3.0 Españahttps://creativecommons.org/licenses/by-nc/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/72232026-06-02T12:44:21Z
dc.title.none.fl_str_mv Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
title Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
spellingShingle Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
Balakrishnan, Narayanaswamy
519.2
Exponential distribution
Minimum density power divergence estimator
Multiple stresses
One-shot devices
Robustness
Wald-type tests
Estadística matemática (Matemáticas)
Probabilidades (Matemáticas)
1209 Estadística
title_short Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
title_full Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
title_fullStr Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
title_full_unstemmed Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
title_sort Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
dc.creator.none.fl_str_mv Balakrishnan, Narayanaswamy
Castilla González, Elena María
Martín Apaolaza, Nirian
Pardo Llorente, Leandro
author Balakrishnan, Narayanaswamy
author_facet Balakrishnan, Narayanaswamy
Castilla González, Elena María
Martín Apaolaza, Nirian
Pardo Llorente, Leandro
author_role author
author2 Castilla González, Elena María
Martín Apaolaza, Nirian
Pardo Llorente, Leandro
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 519.2
Exponential distribution
Minimum density power divergence estimator
Multiple stresses
One-shot devices
Robustness
Wald-type tests
Estadística matemática (Matemáticas)
Probabilidades (Matemáticas)
1209 Estadística
topic 519.2
Exponential distribution
Minimum density power divergence estimator
Multiple stresses
One-shot devices
Robustness
Wald-type tests
Estadística matemática (Matemáticas)
Probabilidades (Matemáticas)
1209 Estadística
description Introduced robust density-based estimators in the context of one-shot devices with exponential lifetimes under a single stress factor. However, it is usual to have several stress factors in industrial experiments involving one-shot devices. In this paper, the weighted minimum density power divergence estimators (WMDPDEs) are developed as a natural extension of the classical maximum likelihood estimators (MLEs) for one-shot device testing data under exponential lifetime model with multiple stresses. Based on these estimators, Wald-type test statistics are also developed. Through a simulation study, it is shown that some WMDPDEs have a better performance than the MLE in relation to robustness. Two examples with multiple stresses show the usefulness of the model and, in particular, of the proposed estimators, both in engineering and medicine.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-05-20
2020
2020-05-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/7223
url https://hdl.handle.net/20.500.14352/7223
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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