Asymptotic Irrelevance of Initial Conditions for Skorohod Reflection Mapping on the Nonnegative Orthant

• 成果类型: Article
• 署名作者: Kella, Offer; Ramasubramanian, S.
• 署名单位: Hebrew University of Jerusalem; Indian Statistical Institute; Indian Statistical Institute Bangalore
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.1120.0538
• 发表日期: 2012
• 页码: 301-312
• 关键词: NETWORKS; STABILITY; MODEL

摘要

A reflection map, induced by the deterministic Skorohod problem on the nonnegative orthant, is applied to a vector-valued function X on the nonnegative real line and then to a + X, where a is a nonnegative constant vector. A question that was posed over 15 years ago is, under what conditions does the difference between the two resulting regulated functions converge to zero for any choice of a as time diverges. This, in turn, implies that if one imposes enough stochastic structure that ensures that the reflection map applied to a multidimensional process X converges in distribution, then it will also converge in distribution when it is applied to eta + X, where eta is any almost surely finite-valued random vector that may even depend on the process X. In this paper we obtain a useful equivalent characterization of this property. As a result, we are able to identify a natural sufficient condition in terms of the given data X and the constant routing matrix. A similar necessary condition is also indicated. A particular implication of our analysis is that under additional stochastic assumptions, asymptotic irrelevance of the initial condition does not require the existence of a stationary distribution. As immediate corollaries of our (and earlier) results we conclude that under the natural stability conditions, a reflected Levy process as well as a Markov additive process has a unique stationary distribution and converges in distribution to this stationary distribution for every initial condition. Extensions of the sufficient condition are then developed for reflection maps with drift and routing coefficients that may be time- and state-dependent; some implications to multidimensional insurance models are briefly discussed.