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Titlebook: Statistical Distributions in Scientific Work; Volume 4 — Models, S Charles Taillie,Ganapati P. Patil,Bruno A. Baldess Conference proceeding

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The Multimodal Exponential Families of Statistical Catastrophe Theory (the ‘cuspoid’ catastrophes, with Weiner input) and a class of regular exponential families, which are the stationary probability densities of the stochastic catastrophe models. These are called the exponential catastrophe densities. Parameter estimation is examined from the point of view of three
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A Note on the Inverse Gaussian Distributionnd Wald (1947) all seem to have discovered and rediscovered the distribution unbeknownst to each other. In addition to its importance as the first passage time distribution of Brownian motion with positive drift, and its occurrence in sequential analysis, there appears an enormous possibility of its
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Some Properties of the Log-Laplace Distribution by (λ/2) exp(−λ|y|), where −∞ < y < ∞ and λ > 0. The random variable X = exp(Y) is said to have the log-Laplace distribution. In this paper, motivated by the problem of extrapolation to low doses in dose response curves, we obtain an axiomatic characterization of the log-Laplace distribution.
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Compound Distributions Relevant to Life Testingund distributions capable of describing this situation in the univariate case were based on the exponential and the gamma distributions. This paper reports a bivariate study of these compound distributions.
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Distributions Associated with Neutrality Properties for Random Proportionsichlet. This paper reviews a number of results associating distributions with neutrality properties and obtains some generalizations when groups of proportions are neutral to other groups. Attempts to generate distributions for random proportions which are not neutral are also briefly reviewed.
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On the Stirling Distribution of the First Kindthe first kind (SDFK) with parameters n and θ. The distribution is so named because it depends upon F(t , n), the Stirling numbers of the first kind. Patil and Wani (1965) have shown that the SDFK is the distribution of the sum of n independent and identically distributed random variables following
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