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Improved Poisson approximations for word patterns

Part of: Markov processes Combinatorial probability Limit theorems

Published online by Cambridge University Press:  01 July 2016

Anant P. Godbole  and
Andrew A. Schaffner
Anant P. Godbole*
Affiliation:
Michigan Technological University
Andrew A. Schaffner*
Affiliation:
California Polytechnic State University, San Luis Obispo
*
∗Postal address: Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA.
∗∗Postal address: Department of Mathematics, California Polytechnic State University, San Luis Obispo, CA 93407, USA.
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Abstract

Let X 1, X 2, · ··, Xn be a sequence of n random variables taking values in the ξ -letter alphabet . We consider the number N = N(n, k) of non-overlapping occurrences of a fixed k-letter word under (a) i.i.d. and (b) stationary Markovian hypotheses on the sequence , and use the Stein–Chen method to obtain Poisson approximations for the same. In each case, results and couplings from Barbour et al. (1992) are used to show that the total variation distance between the distribution of N and that of an appropriate Poisson random variable is of order (roughly) O(kS (k)), where S (k) denotes the stationary probability of the word in question. These results vastly improve on the approximations obtained in Godbole (1991). In the Markov case, we also make use of recently obtained eigenvalue bounds on convergence to stationarity due to Diaconis and Stroock (1991) and Fill (1991).

Keywords

NON-OVERLAPPING AND OVERLAPPING OCCURRENCES OF WORD PATTERNSPOISSON APPROXIMATIONSTEIN-CHEN METHODCOUPLINGRATES OF CONVERGENCE TO STATIONARITY

MSC classification

Primary: 60F05: Central limit and other weak theorems
Secondary: 60C05: Combinatorial probability 60J10: Markov chains (discrete-time Markov processes on discrete state spaces)

Information

Type
Research Article
Information
Advances in Applied Probability , Volume 25 , Issue 2 , June 1993 , pp. 334 - 347
Copyright
Copyright © Applied Probability Trust 1993 

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Footnotes

The research of both authors was partially supported by U.S. National Science Foundation REU Grant DMS-9100829.

References

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