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Characterization of convergence rates for the approximation of the stationary distribution of infinite monotone stochastic matrices
- Part of
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- Journal:
- Journal of Applied Probability / Volume 33 / Issue 4 / December 1996
- Published online by Cambridge University Press:
- 14 July 2016, pp. 974-985
- Print publication:
- December 1996
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- Article
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Let P be an infinite irreducible stochastic matrix, recurrent positive and stochastically monotone and Pn be any n × n stochastic matrix with Pn ≧ Tn, where Tn denotes the n × n northwest corner truncation of P. These assumptions imply the existence of limit distributions π and πn for P and Pn respectively. We show that if the Markov chain with transition probability matrix P meets the further condition of geometric recurrence then the exact convergence rate of πn to π can be expressed in terms of the radius of convergence of the generating function of π. As an application of the preceding result, we deal with the random walk on a half line and prove that the assumption of geometric recurrence can be relaxed. We also show that if the i.i.d. input sequence (A(m)) is such that we can find a real number r0 > 1 with
, then the exact convergence rate of πn to π is characterized by r0. Moreover, when the generating function of A is not defined for |z| > 1, we derive an upper bound for the distance between πn and π based on the moments of A.
, then the exact convergence rate of π