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Invariant measures for Markov chains with no irreducibility assumptions

Published online by Cambridge University Press:  14 July 2016

Abstract

Foster's criterion for positive recurrence of irreducible countable space Markov chains is one of the oldest tools in applied probability theory. In various papers in JAP and AAP it has been shown that, under extensions of irreducibility such as ϕ -irreducibility, analogues of and generalizations of Foster's criterion give conditions for the existence of an invariant measure π for general space chains, and for π to have a finite f-moment ∫π (dy)f(y), where f is a general function. In the case f ≡ 1 these cover the question of finiteness of π itself.

In this paper we show that the same conditions imply the same conclusions without any irreducibility assumptions; Foster's criterion forces sufficient and appropriate regularity on the space automatically. The proofs involve detailed consideration of the structure of the minimal subinvariant measures of the chain.

The results are applied to random coefficient autoregressive processes in order to illustrate the need to remove irreducibility conditions if possible.

Type
Part 6 - The Analysis of Stochastic Phenomena
Copyright
Copyright © Applied Probability Trust 1988 

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References

[1] Chan, K. S., Petruccelli, J., Tong, H., and Woolford, S. W. (1985) A multiplethreshold AR(1) model. J. Appl. Prob. 22, 267269.Google Scholar
[2] Feigin, P. D. and Tweedie, R. L. (1985) Random coefficient autoregressive processes: a Markov chain analysis of stationarity and finiteness of moments. J. Time Series Anal. 6, 114.Google Scholar
[3] Foster, F. G. (1953) On the stochastic matrices associated with certain queueing processes. Ann. Math. Statist. 24, 355360.Google Scholar
[4] Kendall, D. G. (1951) Some problems in the theory of queues. J. R. Statist. Soc. B 13, 151185.Google Scholar
[5] Laslett, G. M., Pollard, D. B. and Tweedie, R. L. (1978) Techniques for establishing ergodic and recurrence properties of continuous-valued Markov chains. Nav. Res. Log. Quart. 25, 455472.Google Scholar
[6] Nicholls, D. F. and Quinn, B. G. (1982) Random Coefficient Autoregressive Models: An Introduction., Lecture Notes in Statistics 11, Springer-Verlag, New York.Google Scholar
[7] Orey, S. (1971) Lecture Notes on Limit Theorems for Markov Chains on a General State Space. Van Nostrand Reinhold, London.Google Scholar
[8] Rosenblatt, M. (1973) Invariant and subinvariant measures of transition probability functions acting on continuous functions. Z. Wahrscheinlichkeitsth. 25, 209221.Google Scholar
[9] Rosenblatt, , (1974) Recurrent points and transition functions acting on continuous functions. Z. Wahrscheinlichkeitsth. 30, 173183.Google Scholar
[10] Seneta, E. and Tweedie, R. L. (1985) Moments for stationary and quasi-stationary distributions of Markov chains. J. Appl. Prob. 22, 148155.Google Scholar
[11] Tuominen, P. and Tweedie, R. L. (1979) Markov chains with continuous components. Proc. Lond. Math. Soc. (3) 38, 89114.Google Scholar
[12] Tweedie, R. L. (1975) Sufficient conditions for ergodicity and recurrence of Markov chains on a general state space. Stoch. Proc. Appl. 3, 385403.Google Scholar
[13] Tweedie, R. L. (1976) Criteria for classifying general Markov chains. Adv. Appl. Prob. 8, 737771.Google Scholar
[14] Tweedie, R. L. (1983) The existence of moments for stationary Markov chains. J. Appl. Prob. 20, 191196.Google Scholar