∈-entropy of sets of probability distribution functions and their Fourier-Stieltjes transforms
Margrit Gauglhofer, A. T. Bharucha-Reid (1973)
Annales de l'I.H.P. Probabilités et statistiques
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Margrit Gauglhofer, A. T. Bharucha-Reid (1973)
Annales de l'I.H.P. Probabilités et statistiques
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Giurgescu, Patricia (2000)
International Journal of Mathematics and Mathematical Sciences
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María Luisa Menéndez, Domingo Morales, Leandro Pardo, Miquel Salicrú (1997)
Applications of Mathematics
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Burbea and Rao (1982a, 1982b) gave some general methods for constructing quadratic differential metrics on probability spaces. Using these methods, they obtained the Fisher information metric as a particular case. In this paper we apply the method based on entropy measures to obtain a Riemannian metric based on -entropy measures (Salicrú et al., 1993). The geodesic distances based on that information metric have been computed for a number of parametric families of distributions. The...
Martin Adamčík (2019)
Kybernetika
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In this paper we present a result that relates merging of closed convex sets of discrete probability functions respectively by the squared Euclidean distance and the Kullback-Leibler divergence, using an inspiration from the Rényi entropy. While selecting the probability function with the highest Shannon entropy appears to be a convincingly justified way of representing a closed convex set of probability functions, the discussion on how to represent several closed convex sets of probability...
Chakrabarti, C.G., Chakrabarty, Indranil (2005)
International Journal of Mathematics and Mathematical Sciences
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María Dolores Esteban (1997)
Applications of Mathematics
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To study the asymptotic properties of entropy estimates, we use a unified expression, called the -entropy. Asymptotic distributions for these statistics are given in several cases when maximum likelihood estimators are considered, so they can be used to construct confidence intervals and to test statistical hypotheses based on one or more samples. These results can also be applied to multinomial populations.