Una prueba de rachas para la alternativa "estocásticamente mayor que" en muestras de la distribución lognormal
A run test for the alternative “stochastically greater than ”in samples of the lognormal distribution
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Abstract (en)
We propose a runs test for the hypothesis of symmetry around an unknown median with alternative “stochastically larger than”based on a trimmed runs test for the hypothesis of symmetry around an unknown median with two-tailed alternative proposed in Babativa & Corzo (2010). By a simulation study we show that for samples coming from the Lognormal Distribution, the proposed test maintains the prefixed size and that its empirical power is larger than of the other compared tests proposed in Cabilio & Masaro (1996), Mira (1999) and Miao et al. (2006).
Abstract (es)
Se propone una prueba de rachas para la hipótesis de simetría alrededor de una mediana desconocida con alternativa de "estocasticamente mayor que" basada en una prueba de rachas recortada para la hipótesis de simetría alrededor de una mediana conocida con alternativa de dos colas propuesta en Babativa & Corzo
(2010). Por medio de un estudio de simulación se muestra que para muestras de la distribución lognormal la prueba propuesta mantiene el tamaño bajo la hipótesis de simetría y que su potencia empírica supera la de las pruebas propuestas en Cabilio & Masaro (1996), Mira (1999) y Miao et al. (2006).
References
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Babativa, J. (2008), Propuesta de una prueba de rachas recortada para hipótesis de simetría, Tesis de Maestría, Universidad Nacional de Colombia, Facultad de Ciencias. Departamento de Estadística, Bogotá.
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Mira, A. (1999), 'Distribution-free test for symmetry based on Bonferroni's measure', Journal of Applied Statistics 26(8), 959-972.
Modarres, R. & Gastwirth (1996), 'A modied runs tes for symmetry', Statistics and probability 25(5), 575-585.
Modarres, R. & Gastwirth, J. (1998), 'Hybrid test for the hypothesis of symmetry', Journal of Applied Statistics 25(6), 777-783.
Welch, B. L. (1938), 'The signicance of the dierence between two means when the population variances are unequal', Biometrika 29, 350-362.
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