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On Testing Dependence between Time to Failure and Cause of Failure when Causes of Failure Are Missing

The hypothesis of independence between the failure time and the cause of failure is studied by using the conditional probabilities of failure due to a specific cause given that there is no failure up to certain fixed time. In practice, there are situations when the failure times are available for al...

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Detalles Bibliográficos
Autores principales: Dewan, Isha, Kulathinal, Sangita
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2092381/
https://www.ncbi.nlm.nih.gov/pubmed/18060052
http://dx.doi.org/10.1371/journal.pone.0001255
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author Dewan, Isha
Kulathinal, Sangita
author_facet Dewan, Isha
Kulathinal, Sangita
author_sort Dewan, Isha
collection PubMed
description The hypothesis of independence between the failure time and the cause of failure is studied by using the conditional probabilities of failure due to a specific cause given that there is no failure up to certain fixed time. In practice, there are situations when the failure times are available for all units but the causes of failures might be missing for some units. We propose tests based on U-statistics to test for independence of the failure time and the cause of failure in the competing risks model when all the causes of failure cannot be observed. The asymptotic distribution is normal in each case. Simulation studies look at power comparisons for the proposed tests for two families of distributions. The one-sided and the two-sided tests based on Kendall type statistic perform exceedingly well in detecting departures from independence.
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spelling pubmed-20923812007-12-05 On Testing Dependence between Time to Failure and Cause of Failure when Causes of Failure Are Missing Dewan, Isha Kulathinal, Sangita PLoS One Research Article The hypothesis of independence between the failure time and the cause of failure is studied by using the conditional probabilities of failure due to a specific cause given that there is no failure up to certain fixed time. In practice, there are situations when the failure times are available for all units but the causes of failures might be missing for some units. We propose tests based on U-statistics to test for independence of the failure time and the cause of failure in the competing risks model when all the causes of failure cannot be observed. The asymptotic distribution is normal in each case. Simulation studies look at power comparisons for the proposed tests for two families of distributions. The one-sided and the two-sided tests based on Kendall type statistic perform exceedingly well in detecting departures from independence. Public Library of Science 2007-12-05 /pmc/articles/PMC2092381/ /pubmed/18060052 http://dx.doi.org/10.1371/journal.pone.0001255 Text en Dewan, Kulathinal. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Dewan, Isha
Kulathinal, Sangita
On Testing Dependence between Time to Failure and Cause of Failure when Causes of Failure Are Missing
title On Testing Dependence between Time to Failure and Cause of Failure when Causes of Failure Are Missing
title_full On Testing Dependence between Time to Failure and Cause of Failure when Causes of Failure Are Missing
title_fullStr On Testing Dependence between Time to Failure and Cause of Failure when Causes of Failure Are Missing
title_full_unstemmed On Testing Dependence between Time to Failure and Cause of Failure when Causes of Failure Are Missing
title_short On Testing Dependence between Time to Failure and Cause of Failure when Causes of Failure Are Missing
title_sort on testing dependence between time to failure and cause of failure when causes of failure are missing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2092381/
https://www.ncbi.nlm.nih.gov/pubmed/18060052
http://dx.doi.org/10.1371/journal.pone.0001255
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