Perfect simulation of autoregressive models with infinite memory

Abstract

In this paper we consider the problem of determining the law of binary stochastic processes from transition kernels depending on the whole past. These kernels are linear in the past values of the process. They are allowed to assume values close to both 0 and 1, preventing the application of usual results on uniqueness. More precisely we give sufficient conditions for uniqueness and non-uniqueness. In the former case a perfect simulation algorithm is also given.

12 pages

References in corpus (2)

• Perfect simulation of infinite range Gibbs measures and coupling with their finite range approximations
• Developments in perfect simulation of Gibbs measures through a new result for the extinction of Galton-Watson-like processes

Cited by in corpus (1)

• One-dimensional infinite memory imitation models with noise