Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum Distributions

<p dir="ltr">Competing risk models are essential in survival analysis for studying systems with multiple mutually exclusive failure events. This study investigates the application of competing risk models in the presence of progressively Type-II censored data for the Dagum distributi...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Raghd Badwan (21751574) (author)
مؤلفون آخرون: Reza Pakyari (20898827) (author)
منشور في: 2025
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author Raghd Badwan (21751574)
author2 Reza Pakyari (20898827)
author2_role author
author_facet Raghd Badwan (21751574)
Reza Pakyari (20898827)
author_role author
dc.creator.none.fl_str_mv Raghd Badwan (21751574)
Reza Pakyari (20898827)
dc.date.none.fl_str_mv 2025-06-30T00:00:00Z
dc.identifier.none.fl_str_mv 10.3390/axioms14070508
dc.relation.none.fl_str_mv https://figshare.com/articles/journal_contribution/Analyzing_Competing_Risks_with_Progressively_Type-IICensored_Data_in_Dagum_Distributions/29606174
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Mathematical sciences
Statistics
bootstrap confidence regions
Maximum Likelihood ApproachMaximum Likelihood
Monte Carlo simulation, cooperation
censoring present
relative risk index
dc.title.none.fl_str_mv Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum Distributions
dc.type.none.fl_str_mv Text
Journal contribution
info:eu-repo/semantics/publishedVersion
text
contribution to journal
description <p dir="ltr">Competing risk models are essential in survival analysis for studying systems with multiple mutually exclusive failure events. This study investigates the application of competing risk models in the presence of progressively Type-II censored data for the Dagum distribution, a flexible distribution suited for modeling data with heavy tails and varying skewness and kurtosis. The methodology includes maximum likelihood estimation of the unknown parameters, with a focus on the special case of a common shape parameter, which allows for a closed-form expression of the relative risks. A hypothesis test is developed to assess the validity of this assumption, and both asymptotic and bootstrap confidence intervals are constructed. The performance of the proposed methods is evaluated through Monte Carlo simulations, and their applicability is demonstrated with a real-world example.</p><h2>Other Information</h2><p dir="ltr">Published in: Axioms<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.3390/axioms14070508" target="_blank">https://doi.org/10.3390/axioms14070508</a></p>
eu_rights_str_mv openAccess
id Manara2_0d89318e59e379acbff10ddb00f02ad3
identifier_str_mv 10.3390/axioms14070508
network_acronym_str Manara2
network_name_str Manara2
oai_identifier_str oai:figshare.com:article/29606174
publishDate 2025
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spelling Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum DistributionsRaghd Badwan (21751574)Reza Pakyari (20898827)Mathematical sciencesStatisticsbootstrap confidence regionsMaximum Likelihood ApproachMaximum LikelihoodMonte Carlo simulation, cooperationcensoring presentrelative risk index<p dir="ltr">Competing risk models are essential in survival analysis for studying systems with multiple mutually exclusive failure events. This study investigates the application of competing risk models in the presence of progressively Type-II censored data for the Dagum distribution, a flexible distribution suited for modeling data with heavy tails and varying skewness and kurtosis. The methodology includes maximum likelihood estimation of the unknown parameters, with a focus on the special case of a common shape parameter, which allows for a closed-form expression of the relative risks. A hypothesis test is developed to assess the validity of this assumption, and both asymptotic and bootstrap confidence intervals are constructed. The performance of the proposed methods is evaluated through Monte Carlo simulations, and their applicability is demonstrated with a real-world example.</p><h2>Other Information</h2><p dir="ltr">Published in: Axioms<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.3390/axioms14070508" target="_blank">https://doi.org/10.3390/axioms14070508</a></p>2025-06-30T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/axioms14070508https://figshare.com/articles/journal_contribution/Analyzing_Competing_Risks_with_Progressively_Type-IICensored_Data_in_Dagum_Distributions/29606174CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296061742025-06-30T00:00:00Z
spellingShingle Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum Distributions
Raghd Badwan (21751574)
Mathematical sciences
Statistics
bootstrap confidence regions
Maximum Likelihood ApproachMaximum Likelihood
Monte Carlo simulation, cooperation
censoring present
relative risk index
status_str publishedVersion
title Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum Distributions
title_full Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum Distributions
title_fullStr Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum Distributions
title_full_unstemmed Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum Distributions
title_short Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum Distributions
title_sort Analyzing Competing Risks with Progressively Type-IICensored Data in Dagum Distributions
topic Mathematical sciences
Statistics
bootstrap confidence regions
Maximum Likelihood ApproachMaximum Likelihood
Monte Carlo simulation, cooperation
censoring present
relative risk index