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

Claudia Castro-Kuriss, Diana M. Kelmansky, Víctor Leiva, Elena J. Martínez

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

10 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)1193-1211
Número de páginas19
PublicaciónJournal of Applied Statistics
Volumen37
N.º7
DOI
EstadoPublicada - jul. 2010
Publicado de forma externa

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