Stability Problems for Stochastic Models : Proceedings of the 8th International Seminar held in Uzhgorod, USSR, Sept. 23-29, 1984

ISBN 10: 3662167948 / ISBN 13: 9783662167946

Published by Springer, 2014

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Bibliographic Details

• Title: Stability Problems for Stochastic Models : Proceedings of the 8th International Seminar held in Uzhgorod, USSR, Sept. 23-29, 1984
• Publisher: Springer
• Publication Date: 2014
• Language: English
• ISBN 10: 3662167948
• ISBN 13: 9783662167946
• Binding: Soft cover
• Condition: As New

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Synopsis

On periodic distribution of waiting-time process.- Asymptotic behaviour of some random fields.- Normality of a multidimensional infinitely divisible distribution which coincides with a normal distribution in a cone.- Partial sufficiency of linear forms.- A refinement of Darmois-Skitovitch and Heyde theorems.- The rate of convergence of Erlang distribution to the degenerate one.- Metric estimates of the first occurence time in regenerative processes.- Stability of the lack-of-memory property in a finite number of points.- The central limit theorem and weak convergence of maxima of sums of independent random variables.- Compound metrics with fixed minimal ones.- On limit theorems for the number of Markov renewals.- Some characterization of bivariate distributions based on hazard rate properties.- Confidence intervals for the parameters of strongly stable laws.- Generalization of the Titchmarsh convolution theorem and the complex-valued measures uniquely determined by their restrictions to a half-line.- Limit theorems for extreme order statistics under nonlinear normalization.- A characterization of Gaussian distribution by the linearity of conditional moments.- Extreme functionals in the space of probability measures.- On the ratio of independent stable random variables.- On the Berry-Esseen bound for Student's statistic.- Some new results in a theorem due to Renyi.- Stability for continuous-time nonhomogeneous Markov chains.- Rate of convergence in limit theorems for the max-scheme.- On compound probabilistic metrics with given minimal metrics.