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Poisson approximation for (k 1, k 2)-events via the Stein-Chen method

Part of:Distribution theory - ProbabilityLimit theoremsDistribution theory

Published online by Cambridge University Press:  14 July 2016

P. Vellaisamy
P. Vellaisamy*
Affiliation:
Indian Institute of Technology, Bombay
*
∗ Postal address: Department of Mathematics, Indian Institute of Technology, Bombay, Powai, Mumbai 400 076, India. Email address: pv@math.iitb.ac.in
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Abstract

Consider a sequence of independent Bernoulli trials with success probability p. Let N(n; k 1, k 2) denote the number of times that k 1 failures are followed by k 2 successes among the first n Bernoulli trials. We employ the Stein-Chen method to obtain a total variation upper bound for the rate of convergence of N(n; k 1, k 2) to a suitable Poisson random variable. As a special case, the corresponding limit theorem is established. Similar results are obtained for N k3 (n; k 1, k 2), the number of times that k 1 failures followed by k 2 successes occur k 3 times successively in n Bernoulli trials. The bounds obtained are generally sharper than, and improve upon, some of the already known results. Finally, the technique is adapted to obtain Poisson approximation results for the occurrences of the above-mentioned events under Markov-dependent trials.

Keywords

(k 1, k 2)-eventbinomial distribution of order (k 1, k 2)Poisson approximationStein-Chen methodMarkov-Bernoulli variablerate of convergencelimit theorem

MSC classification

Primary:62E17: Approximations to distributions (nonasymptotic)62E20: Asymptotic distribution theory
Secondary:60E05: Distributions60F05: Central limit and other weak theorems

Information

Type
Research Papers
Information
Journal of Applied Probability , Volume 41 , Issue 4 , December 2004, pp. 1081 - 1092
Copyright
Copyright © Applied Probability Trust 2004 

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