A new goodness-of-fit test for censored data with an application in monitoring processes

In this article, we propose a new goodness-of-fit test for Type I or Type II censored samples from a completely specified distribution. This test is a generalization of Michael's test for censored data, which is based on the empirical distribution and a variance stabilizing transformation. Usin...

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Detalles Bibliográficos
Publicado: 2009
Materias:
And stabilized probability plots
Kolmogorov-Smirnov test
Monte Carlo simulation
PP
QQ
Quality control
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03610918_v38_n6_p1161_CastroKuriss
http://hdl.handle.net/20.500.12110/paper_03610918_v38_n6_p1161_CastroKuriss
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_03610918_v38_n6_p1161_CastroKuriss
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spelling paper:paper_03610918_v38_n6_p1161_CastroKuriss2023-06-08T15:34:51Z A new goodness-of-fit test for censored data with an application in monitoring processes And stabilized probability plots Kolmogorov-Smirnov test Monte Carlo simulation PP QQ Quality control In this article, we propose a new goodness-of-fit test for Type I or Type II censored samples from a completely specified distribution. This test is a generalization of Michael's test for censored data, which is based on the empirical distribution and a variance stabilizing transformation. Using Monte Carlo methods, the distributions of the test statistics are analyzed under the null hypothesis. Tables of quantiles of these statistics are also provided. The power of the proposed test is studied and compared to that of other well-known tests also using simulation. The proposed test is more powerful in most of the considered cases. Acceptance regions for the PP, QQ, and Michael's stabilized probability plots are derived, which enable one to visualize which data contribute to the decision of rejecting the null hypothesis. Finally, an application in quality control is presented as illustration. 2009 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03610918_v38_n6_p1161_CastroKuriss http://hdl.handle.net/20.500.12110/paper_03610918_v38_n6_p1161_CastroKuriss
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic And stabilized probability plots
Kolmogorov-Smirnov test
Monte Carlo simulation
PP
QQ
Quality control
spellingShingle And stabilized probability plots
Kolmogorov-Smirnov test
Monte Carlo simulation
PP
QQ
Quality control
A new goodness-of-fit test for censored data with an application in monitoring processes
topic_facet And stabilized probability plots
Kolmogorov-Smirnov test
Monte Carlo simulation
PP
QQ
Quality control
description In this article, we propose a new goodness-of-fit test for Type I or Type II censored samples from a completely specified distribution. This test is a generalization of Michael's test for censored data, which is based on the empirical distribution and a variance stabilizing transformation. Using Monte Carlo methods, the distributions of the test statistics are analyzed under the null hypothesis. Tables of quantiles of these statistics are also provided. The power of the proposed test is studied and compared to that of other well-known tests also using simulation. The proposed test is more powerful in most of the considered cases. Acceptance regions for the PP, QQ, and Michael's stabilized probability plots are derived, which enable one to visualize which data contribute to the decision of rejecting the null hypothesis. Finally, an application in quality control is presented as illustration.
title A new goodness-of-fit test for censored data with an application in monitoring processes
title_short A new goodness-of-fit test for censored data with an application in monitoring processes
title_full A new goodness-of-fit test for censored data with an application in monitoring processes
title_fullStr A new goodness-of-fit test for censored data with an application in monitoring processes
title_full_unstemmed A new goodness-of-fit test for censored data with an application in monitoring processes
title_sort new goodness-of-fit test for censored data with an application in monitoring processes
publishDate 2009
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03610918_v38_n6_p1161_CastroKuriss
http://hdl.handle.net/20.500.12110/paper_03610918_v38_n6_p1161_CastroKuriss
_version_ 1768541562637123584

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