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Let {X,, n 1} be a strictly stationary sequence of random variables. Denote

From Strong-Mixing Processes, Wiesław Dziubdziela

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Abstract

We present necessary and sufficient conditions for the weak convergence of the distributions of the kth order statistics from a strictly stationary strongmixing sequence of random variables to limit laws which are represented in terms of a compound Poisson distribution. The obtained limit laws form a class larger than that occurring in the independent case.

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We present necessary and sufficient conditions for the weak convergence of the distributions of the kth order statistics from a strictly stationary strongmixing sequence of random variables to limit laws which are represented in terms of a compound Poisson distribution. The obtained limit laws form a class larger than that occurring in the independent case.

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Available abstract

We present necessary and sufficient conditions for the weak convergence of the distributions of the kth order statistics from a strictly stationary strongmixing sequence of random variables to limit laws which are represented in terms of a compound Poisson distribution. The obtained limit laws form a class larger than that occurring in the independent case.

Key concepts: Mathematics, Stationary sequence, Random variable, Sequence (biology), Limit (mathematics), Convergence of random variables, Poisson distribution, Limit of a function

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Let {X,, n 1} be a strictly stationary sequence of random variables. Denote — Research Paper | ScholarLens