On a goodness-of-fit test for normality with unknown parameters and type-II censored data

"We propose a new goodness-of-fit test for normal and lognormal distributions with unknown parameters and type-II censored data. This test is a generalization of Michael's test for censored samples, which is based on the empirical distribution and a variance stabilizing transformation. We...

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Autores principales: Castro-Kuriss, Claudia, Kelmansky, Diana M., Leiva, Víctor, Martínez, Elena J.
Formato: Artículos de Publicaciones Periódicas acceptedVersion
Lenguaje:Inglés
Publicado: 2022
Materias:
ESTADISTICA
PRUEBAS
METODOS DE MONTE CARLO
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/3843
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-3843
record_format dspace
spelling I32-R138-123456789-38432022-12-07T13:06:59Z On a goodness-of-fit test for normality with unknown parameters and type-II censored data Castro-Kuriss, Claudia Kelmansky, Diana M. Leiva, Víctor Martínez, Elena J. ESTADISTICA PRUEBAS METODOS DE MONTE CARLO "We propose a new goodness-of-fit test for normal and lognormal distributions with unknown parameters and type-II censored data. This test is a generalization of Michael's test for censored samples, which is based on the empirical distribution and a variance stabilizing transformation. We estimate the parameters of the model by using maximum likelihood and Gupta's methods. The quantiles of the distribution of the test statistic under the null hypothesis are obtained through Monte Carlo simulations. The power of the proposed test is estimated and compared to that of the Kolmogorov-Smirnov test also using simulations. The new test is more powerful than the Kolmogorov-Smirnov test in most of the studied cases. Acceptance regions for the PP, QQ and Michael's stabilized probability plots are derived, making it possible to visualize which data contribute to the decision of rejecting the null hypothesis. Finally, an illustrative example is presented." 2022-05-02T16:01:38Z 2022-05-02T16:01:38Z 2010-07 Artículos de Publicaciones Periódicas info:eu-repo/semantics/acceptedVersion 1360-0532 http://ri.itba.edu.ar/handle/123456789/3843 en info:eu-repo/semantics/altIdentifier/doi/10.1080/02664760902984626 info:eu-repo/grantAgreement/ANPCyT/PICT/21407/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/grantAgreement/UBA/UBACyT/X-018 /AR. Ciudad Autónoma de Buenos Aires info:eu-repo/grantAgreement/CONICET/PIP/5505/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/grantAgreement/FONDECYT/1080326/CL. Santiago de Chile info:eu-repo/grantAgreement/DIPUV/29-2006/CL. Santiago de Chile application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic ESTADISTICA
PRUEBAS
METODOS DE MONTE CARLO
spellingShingle ESTADISTICA
PRUEBAS
METODOS DE MONTE CARLO
Castro-Kuriss, Claudia
Kelmansky, Diana M.
Leiva, Víctor
Martínez, Elena J.
On a goodness-of-fit test for normality with unknown parameters and type-II censored data
topic_facet ESTADISTICA
PRUEBAS
METODOS DE MONTE CARLO
description "We propose a new goodness-of-fit test for normal and lognormal distributions with unknown parameters and type-II censored data. This test is a generalization of Michael's test for censored samples, which is based on the empirical distribution and a variance stabilizing transformation. We estimate the parameters of the model by using maximum likelihood and Gupta's methods. The quantiles of the distribution of the test statistic under the null hypothesis are obtained through Monte Carlo simulations. The power of the proposed test is estimated and compared to that of the Kolmogorov-Smirnov test also using simulations. The new test is more powerful than the Kolmogorov-Smirnov test in most of the studied cases. Acceptance regions for the PP, QQ and Michael's stabilized probability plots are derived, making it possible to visualize which data contribute to the decision of rejecting the null hypothesis. Finally, an illustrative example is presented."
format Artículos de Publicaciones Periódicas
acceptedVersion
author Castro-Kuriss, Claudia
Kelmansky, Diana M.
Leiva, Víctor
Martínez, Elena J.
author_facet Castro-Kuriss, Claudia
Kelmansky, Diana M.
Leiva, Víctor
Martínez, Elena J.
author_sort Castro-Kuriss, Claudia
title On a goodness-of-fit test for normality with unknown parameters and type-II censored data
title_short On a goodness-of-fit test for normality with unknown parameters and type-II censored data
title_full On a goodness-of-fit test for normality with unknown parameters and type-II censored data
title_fullStr On a goodness-of-fit test for normality with unknown parameters and type-II censored data
title_full_unstemmed On a goodness-of-fit test for normality with unknown parameters and type-II censored data
title_sort on a goodness-of-fit test for normality with unknown parameters and type-ii censored data
publishDate 2022
url http://ri.itba.edu.ar/handle/123456789/3843
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AT kelmanskydianam onagoodnessoffittestfornormalitywithunknownparametersandtypeiicensoreddata
AT leivavictor onagoodnessoffittestfornormalitywithunknownparametersandtypeiicensoreddata
AT martinezelenaj onagoodnessoffittestfornormalitywithunknownparametersandtypeiicensoreddata
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