Abstract
Let {Xn, n= 0, 1, 2, ···} be a transient Markov chain which, when restricted to the state space 𝒩+= {1, 2, ···}, is governed by an irreducible, aperiodic and strictly substochastic matrix𝐏= (pij), and letpij(n) =P∈Xn=j, Xk∈ 𝒩+fork= 0, 1, ···,n|X0=i],i, j𝒩+. The prime concern of this paper is conditions for the existence of the limits,qijsay, ofasn →∞. Ifthe distribution (qij) is called the quasi-stationary distribution of {Xn} and has considerable practical importance. It will be shown that, under some conditions, if a non-negative non-trivial vectorx= (xi) satisfyingrxT=xT𝐏andexists, whereris the convergence norm of𝐏, i.e.r=R–1andand T denotes transpose, then it is unique, positive elementwise, andqij(n) necessarily converge toxjasn →∞.Unlike existing results in the literature, our results can be applied even to theR-null andR-transient cases. Finally, an application to a left-continuous random walk whose governing substochastic matrix isR-transient is discussed to demonstrate the usefulness of our results.
Publisher
Cambridge University Press (CUP)
Subject
Statistics, Probability and Uncertainty,General Mathematics,Statistics and Probability
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Index;Orthogonal Polynomials in the Spectral Analysis of Markov Processes;2021-10-31
2. References;Orthogonal Polynomials in the Spectral Analysis of Markov Processes;2021-10-31
3. Spectral Representation of Diffusion Processes;Orthogonal Polynomials in the Spectral Analysis of Markov Processes;2021-10-31
4. Spectral Representation of Birth–Death Processes;Orthogonal Polynomials in the Spectral Analysis of Markov Processes;2021-10-31
5. Small Phenomena, Big Implications;Orthogonal Polynomials in the Spectral Analysis of Markov Processes;2021-10-31