Displaying similar documents to “Dispersive functions and stochastic orders”

Information Matrix for Beta Distributions

Aryal, Gokarna, Nadarajah, Saralees (2004)

Serdica Mathematical Journal

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2000 Mathematics Subject Classification: 33C90, 62E99. The Fisher information matrix for three generalized beta distributions are derived.

Bounds on tail probabilities for negative binomial distributions

Peter Harremoës (2016)

Kybernetika

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In this paper we derive various bounds on tail probabilities of distributions for which the generated exponential family has a linear or quadratic variance function. The main result is an inequality relating the signed log-likelihood of a negative binomial distribution with the signed log-likelihood of a Gamma distribution. This bound leads to a new bound on the signed log-likelihood of a binomial distribution compared with a Poisson distribution that can be used to prove an intersection...

Characterizations of inequality orderings by means of dispersive orderings.

Héctor M. Ramos Romero, Miguel Angel Sordo Díaz (2002)

Qüestiió

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The generalized Lorenz order and the absolute Lorenz order are used in economics to compare income distributions in terms of social welfare. In Section 2, we show that these orders are equivalent to two stochastic orders, the concave order and the dilation order, which are used to compare the dispersion of probability distributions. In Section 3, a sufficient condition for the absolute Lorenz order, which is often easy to verify in practice, is presented. This condition is applied in...

Invariance of relative inverse function orderings under compositions of distributions

Magdalena Frąszczak, Jarosław Bartoszewicz (2012)

Applicationes Mathematicae

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Bartoszewicz and Benduch (2009) applied an idea of Lehmann and Rojo (1992) to a new setting and used the GTTT transform to define invariance properties and distances of some stochastic orders. In this paper Lehmann and Rojo's idea is applied to the class of models which is based on distributions which are compositions of distribution functions on [0,1] with underlying distributions. Some stochastic orders are invariant with respect to these models.