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Continuous, discrete, and conditional scan statistics. (English) Zbl 1236.60016
Summary: The distributions for continuous, discrete, and conditional discrete scan statistics are studied. The approach of finite Markov chain imbedding, which has been applied to random permutations as well as to runs and patterns, is extended to compute the distribution of the conditional discrete scan statistic, defined from a sequence of Bernoulli trials. It is shown that the distribution of the continuous scan statistic induced by a Poisson process defined on \((0, 1]\) is a limiting distribution of weighted distributions of conditional discrete scan statistics. Comparisons of rates of convergence as well as numerical comparisons of various bounds and approximations are provided to illustrate the theoretical results.

MSC:
60E05 Probability distributions: general theory
60J10 Markov chains (discrete-time Markov processes on discrete state spaces)
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