Abstract
We develop a Bootstrap method in Markov-sequences. This method is based on kernel estimates of the transition density of the Markov-sequence. It is shown that the Bootstrap estimate of the variance of a statistic which is a function of means, is consistent. We also show that the Bootstrap distributions of mean-like statistics and von Mises differentiable statistics converge to appropriate normal distributions. A few simulation results are reported to illustrate the discussion.
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Most of this work was carried out when the author was at the Department of Statistics, Pennsylvania State University.
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Rajarshi, M.B. Bootstrap in Markov-sequences based on estimates of transition density. Ann Inst Stat Math 42, 253–268 (1990). https://doi.org/10.1007/BF00050835
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DOI: https://doi.org/10.1007/BF00050835