TY - JOUR

T1 - Renewal theory for transient markov chains with asymptotically zero drift

AU - Denisov, Denis

AU - Korshunov, Dmitry

AU - Wachtel, Vitali

N1 - Publisher Copyright: © 2020 American Mathematical Society Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/10

Y1 - 2020/10

N2 - We solve the problem of asymptotic behaviour of the renewal measure (Green function) generated by a transient Lamperti's Markov chain Xn in R, that is, when the drift of the chain tends to zero at infinity. Under this setting, the average time spent by Xn in the interval (x, x + 1] is roughly speaking the reciprocal of the drift and tends to infinity as x grows. For the first time we present a general approach relying on a diffusion approximation to prove renewal theorems for Markov chains. We apply a martingale-type technique and show that the asymptotic behaviour of the renewal measure heavily depends on the rate at which the drift vanishes. The two main cases are distinguished, either the drift of the chain decreases as 1/x or much slower than that, say as 1/xα for some α ∈ (0, 1). The intuition behind how the renewal measure behaves in these two cases is totally different. While in the first case Xn2 /n converges weakly to a Γ-distribution and there is no law of large numbers available, in the second case a strong law of large numbers holds true for Xn1+α/n and further normal approximation is available.

AB - We solve the problem of asymptotic behaviour of the renewal measure (Green function) generated by a transient Lamperti's Markov chain Xn in R, that is, when the drift of the chain tends to zero at infinity. Under this setting, the average time spent by Xn in the interval (x, x + 1] is roughly speaking the reciprocal of the drift and tends to infinity as x grows. For the first time we present a general approach relying on a diffusion approximation to prove renewal theorems for Markov chains. We apply a martingale-type technique and show that the asymptotic behaviour of the renewal measure heavily depends on the rate at which the drift vanishes. The two main cases are distinguished, either the drift of the chain decreases as 1/x or much slower than that, say as 1/xα for some α ∈ (0, 1). The intuition behind how the renewal measure behaves in these two cases is totally different. While in the first case Xn2 /n converges weakly to a Γ-distribution and there is no law of large numbers available, in the second case a strong law of large numbers holds true for Xn1+α/n and further normal approximation is available.

KW - Green function

KW - Lamperti's problem

KW - Renewal kernel

KW - Renewal measure

KW - Transient Markov chain

KW - LOCAL LIMIT-THEOREMS

KW - renewal measure

KW - renewal kernel

KW - FUNCTIONALS

UR - http://www.scopus.com/inward/record.url?scp=85092349569&partnerID=8YFLogxK

U2 - 10.1090/tran/8167

DO - 10.1090/tran/8167

M3 - Article

AN - SCOPUS:85092349569

VL - 373

SP - 7253

EP - 7286

JO - Transactions of the American Mathematical Society

JF - Transactions of the American Mathematical Society

SN - 0002-9947

IS - 10

ER -