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On Finite Markov Chain Imbedding and Its Applications

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Abstract

The finite Markov Chain Imbedding technique has been successfully applied in various fields for finding the exact or approximate distributions of runs and patterns under independent and identically distributed or Markov dependent trials. In this paper, we derive a new recursive equation for distribution of scan statistic using the finite Markov chain imbedding technique. We also address the problem of obtaining transition probabilities of the imbedded Markov chain by introducing a notion termed Double Finite Markov Chain Imbedding where transition probabilities are obtained by using the finite Markov chain imbedding technique again. Applications for random permutation model in chemistry and coupon collector’s problem are given to illustrate our idea.

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References

  • Brown M, Peköz EA, Ross SM (2008) A random permutation model arising in chemistry. J Appl Probab 45:1060–1070

    Article  MathSciNet  MATH  Google Scholar 

  • Chang YM, Wu TL (2011) On average run lengths of control charts for autocorrelated processes. Methodol Comput Appl Probab 13:419–431

    Article  MathSciNet  MATH  Google Scholar 

  • Cui L, Xu Y, Zhao X (2010) Developments and applications of the finite Markov Chain imbedding approach in reliability. IEEE Trans Reliab 59:685–690

    Article  Google Scholar 

  • Fu JC (1995) Exact and limiting distributions of the number of successions in a random permutation. Ann Inst Stat Math 47:435–446

    MATH  Google Scholar 

  • Fu JC (1996) Distribution theory of runs and patterns associated with a sequence of multi-state trials. Stat Sin 6:957–974

    MATH  Google Scholar 

  • Fu JC (2001) Distribution of the scan statistic for a sequence of bistate trials. J Appl Probab 38:908–916

    Article  MathSciNet  MATH  Google Scholar 

  • Fu JC, Koutras MV (1994) Distribution theory of runs: a Markov chain approach. J Am Stat Assoc 89:1050–1058

    Article  MathSciNet  MATH  Google Scholar 

  • Fu JC, Lou WYW (2003) Distribution theory of runs and patterns and its applications. World Scientific, River Edge

    MATH  Google Scholar 

  • Fu JC, Wu TL (2010) Linear and nonlinear boundary crossing probabilities for brownian motion and related processes. J Appl Probab 47:1058–1071

    Article  MathSciNet  MATH  Google Scholar 

  • Fu JC, Lou WYW, Wang YJ (1999) On the exact distributions of Eulerian and Simon Newcomb numbers associated with random permutations. Stat Probab Lett 42:115–125

    Article  MathSciNet  MATH  Google Scholar 

  • Johnson BC, Fu JC (2000) The distribution of increasing l-sequences in random permutations: a Markov chain approach. Stat Probab Lett 49:337–344

    Article  MathSciNet  MATH  Google Scholar 

  • Johnson BC, Sellke TM (2010) On the number of i.i.d. samples required to observe all of the balls in an urn. Methodol Comput Appl Probab 12:139–154

    Article  MathSciNet  MATH  Google Scholar 

  • Ross SM, Peköz EA (2007) A second course in probability. Pekozbooks, Boston

    Google Scholar 

  • Scoville R (1966) Mathematical notes: the Hat-Check problem. Am Math Mon 73:262–265

    Article  MathSciNet  Google Scholar 

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Correspondence to Tung-Lung Wu.

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Wu, TL. On Finite Markov Chain Imbedding and Its Applications. Methodol Comput Appl Probab 15, 453–465 (2013). https://doi.org/10.1007/s11009-011-9268-1

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  • DOI: https://doi.org/10.1007/s11009-011-9268-1

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