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The exact and limiting distributions for the number of successes in success runs within a sequence of Markov-dependent two-state trials. (English) Zbl 1067.60064
Summary: The total number of successes in success runs of length greater than or equal to \(k\) in a sequence of \(n\) two-state trials is a statistic that has been broadly used in statistics and probability. For Bernoulli trials with \(k\) equal to one, this statistic has been shown to have binomial and normal distributions as exact and limiting distributions, respectively. For the case of Markov-dependent two-state trials with \(k\) greater than one, its exact and limiting distributions have never been considered in the literature. The finite Markov chain imbedding technique and the invariance principle are used to obtain, in general, the exact and limiting distributions of this statistic under Markov dependence, respectively. Numerical examples are given to illustrate the theoretical results.

MSC:
60J10 Markov chains (discrete-time Markov processes on discrete state spaces)
60F05 Central limit and other weak theorems
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