Extreme values statistics for Markov chains via the (pseudo-) regenerative method

Abstract

This paper is devoted to the study of specific statistical methods for extremal events in the markovian setup, based on the regenerative method and the Nummelin technique. Exploiting ideas developed in [53], the principle underlying our methodology consists in generating first (a random number l of) approximate pseudo-renewal times 1, 2, . . . , l for a sample path X1, . . . , Xn drawn from a Harris chain X with state space E, from the parameters of a minorization condition fulfilled by its transition kernel, and computing then submaxima over the approximate cycles thus obtained: max1+1i2 f(Xi), . . . , max1+l−1il f(Xi) for any measurable function f : E ! R. Estimators of tail features of the sample maximum max1in f(Xi) are then constructed by applying standard statistical methods, tailored for the i.i.d. setting, to the submaxima as if they were independent and identically distributed. In particular, the asymptotic properties of extensions of popular inference procedures based on (conditional) maximum likelihood theory, such as the Hill's method for the index of regular variation, are thoroughly investigated. Using the same approach, here we also consider the problem of estimating the extremal index of the sequence {f(Xn)}n2N under suitable assumptions. Eventually, practical issues related to the application of the methodology we propose are discussed and preliminary simulation results are displayed.