¹Colegio de Postgraduados, Socio Economía Estadística e Informática, Estadística, Texcoco, México. Student. Email: salinas.victor@colpos.mx
²Colegio de Postgraduados, Socio Economía Estadística e Informática, Estadística, Texcoco, México. Professor. Email: perpdgo@colpos.mx
³Colegio de Postgraduados, Socio Economía Estadística e Informática, Estadística, Texcoco, México. Professor. Email: egonzalez@colpos.mx
⁴Colegio de Postgraduados, Socio Economía Estadística e Informática, Estadística, Texcoco, México. Professor. Email: hvaquera@colpos.mx

In this article goodness of fit tests for the Gumbel distribution with type II right censored data are proposed. One test is based in earlier works using the Kullback Leibler information modified for censored data. The other tests are based on the sample correlation coefficient and survival analysis concepts. The critical values of the tests were obtained by Monte Carlo simulation for different sample sizes and percentages of censored data. The powers of the proposed tests were compared under several alternatives. The simulation results show that the test based on the Kullback-Leibler information is superior in terms of power to the correlation tests.

Key words: Correlation coefficient, Entropy, Monte Carlo simulation, Power of a test.

En este artículo se proponen pruebas de bondad de ajuste para la distribución Gumbel para datos censurados por la derecha Tipo II. Una prueba se basa en trabajos previos en los que se modifica la información de Kullback-Leibler para datos censurados. Las otras pruebas se basan en el coeficiente de correlación muestral y en conceptos de análisis de supervivencia. Los valores críticos se obtuvieron mediante simulación Monte Carlo para diferentes tamaños de muestras y porcentajes de censura. La potencia de la pruebas se compararon bajo varias alternativas. Los resultados de la simulación muestran que la prueba basada en la Divergencia de Kullback-Leibler es superior a las pruebas de correlación en términos de potencia.

Palabras clave: coeficiente de correlación, entropía, potencia de una prueba, simulación Monte Carlo.

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