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Quasi-limiting distributions of Markov chains that are skip-free to the left in continuous time

Published online by Cambridge University Press:  14 July 2016

Masaaki Kijima*
Affiliation:
University of Tsukuba, Tokyo
*
Postal address: Graduate School of Systems Management, University of Tsukuba, Tokyo, 3-29-1 Otsuka, Bunkyo-ku, Tokyo 112, Japan.

Abstract

A continuous-time Markov chain on the non-negative integers is called skip-free to the left (right) if the governing infinitesimal generator A = (aij) has the property that aij = 0 for ji ‒ 2 (ij – 2). If a Markov chain is skip-free both to the left and to the right, it is called a birth-death process. Quasi-limiting distributions of birth–death processes have been studied in detail in their own right and from the standpoint of finite approximations. In this paper, we generalize, to some extent, results for birth-death processes to Markov chains that are skip-free to the left in continuous time. In particular the decay parameter of skip-free Markov chains is shown to have a similar representation to the birth-death case and a result on convergence of finite quasi-limiting distributions is obtained.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1993 

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References

[1] Abate, J. and Whitt, W. (1989) Spectral theory for skip-free Markov chains. Prob. Eng. Inf. Sci. 3, 7788.Google Scholar
[2] Anderson, W. J. (1991) Continuous-time Markov Chains: An Applications Oriented Approach. Springer-Verlag, New York.Google Scholar
[3] Brown, M, and Shao, Y. (1987) Identifying coefficients in the spectral representation for first-passage-time distributions. Prob. Eng. Inf. Sci. 1, 6974.Google Scholar
[4] Van Doorn, E. (1987) Representations and bounds for zeros of orthogonal polynomials and eigenvalues of sign-symmetric tri-diagonal matrices. J. Approx. Theory 51, 254266.Google Scholar
[5] Van Doorn, E. (1991) Quasi-stationary distributions and convergence to quasi-stationarity of birth-death processes. Adv. Appl. Prob. 23, 683700.Google Scholar
[6] Karlin, S. and Mcgregor, J. L. (1957) The differential equations of birth-and-death processes, and the Stieltjes moment problem. Trans. Amer. Math. Soc. 85, 489546.Google Scholar
[7] Karlin, S. and Mcgregor, J. L. (1959) A characterization of birth and death processes. Proc. Nat. Acad. Sci USA 45, 375379.Google Scholar
[8] Keilson, J. (1979) Markov Chain Models - Rarity and Exponentiality. Springer-Verlag, New York.Google Scholar
[9] Keilson, J. and Ramaswamy, R. (1986) The bivariate maximum process and quasi-stationary structure of birth-death processes. Stoch. Proc. Appl. 22, 2736.Google Scholar
[10] Kijima, M. (1987) Spectral structure of the first-passage-time densities for classes of Markov chains. J. Appl. Prob. 24, 631643.Google Scholar
[11] Kijima, M. (1992) On the existence of quasi-stationary distributions in denumerable R-transient Markov chains. J. Appl. Prob. 29, 2136.Google Scholar
[12] Kijima, M. (1992) Evaluation of the decay parameter for some specialized birth-death processes. J. Appl. Prob. 29, 781791.Google Scholar
[13] Kijima, M. and Seneta, E. (1991) Some results for quasi-stationary distributions of birth-death processes. J. Appl. Prob. 28, 503511.Google Scholar
[14] Pakes, A. G. (1973) Conditional limit theorems for a left-continuous random walk. J. Appl. Prob. 10, 3957.Google Scholar
[15] Seneta, E. (1981) Non-negative Matrices and Markov Chains, 2nd edn. Springer-Verlag, New York.Google Scholar