Robust Model Selection for Stochastic Processes

Abstract

We address the problem of robust model selection for finite memory stochastic processes. Consider m independent samples, with most of them being realizations of the same stochastic process with law Q, which is the one we want to retrieve. We define the asymptotic breakdown point γ for a model selection procedure and also we devise a model selection procedure. We compute the value of γ which is 0.5, when all the processes are Markovian. This result is valid for any family of finite order Markov models but for simplicity we will focus on the family of variable length Markov chains.