Leandro Pardo Llorente

A necessary power divergence-type family of tests for testing elliptical symmetry

This paper presents a family of power divergence-type test statistics for testing the hypothesis of elliptical symmetry. We assess the performance of the new family of test statistics, using Monte Carlo simulation. In this context, the type I error rate as well as the power of the tests are studied. Specifically, for selected alternatives, we compare the power of the proposed procedure with that proposed by Schott [Testing for elliptical symmetry in covariance-matrix-based analyses, Stat. Probab. Lett. 60 (2002), pp. 395–404]. This last test statistic is an easily computed one with a tractable null distribution and very good power for various alternatives, as it has established in previous published simulations studies [F. Huffer and C. Park, A test for elliptical symmetry, J. Multivariate Anal. 98 (2007), pp. 256–281; L. Sakhanenko, Testing for ellipsoidal symmetry: A comparison study, Comput. Stat. Data Anal. 53 (2008), pp. 565–581]. Finally, a well-known real data set is used to illustrate the method developed in this paper.

Energia informacional util

ResumenEn este trabajo se adapta la medida de Información dada por Onicescu en 1966, denominada Energía Informacional, a situaciones en las cuales existe una función de utilidad definida sobre los resultados del experimento; a esta medida de Información la denominamos ENERGIA INFORMACIONAL UTIL.

Medidas de nitidez para conjuntos difusos

ResumenEn este trabajo se realiza un estudio de Medidas de Nitidez para conjuntos difusos. Se comienza dando los conceptos de Medida Puntual de Nitidez o Autonitidez puntual y Medida de Nitidez para conjunto difuso, pasando a continuación a dar dos teoremas de construcción de Medidas de Nitidez y uno de caracterización para aquellas medidas que sean valoraciones en el retículo,Ln(X).

Phi-divergence-type test for positive dependence alternatives in 2 x k contingency tables

In this chapter, we consider 2×k contingency tables and derive a new family of test statistics for detecting positive dependence in them. The family of test statistics introduced here is based on the φ-divergence measures of which the likelihood ratio test is a special case.