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Rate of convergence for computing expectations of stopping functionals of an α-mixing process

Part of:Limit theorems

Published online by Cambridge University Press:  01 July 2016

Mohamed Ben Alaya and
Gilles Pagès
Mohamed Ben Alaya*
Affiliation:
LAGA URA 742 and CERMICS
Gilles Pagès*
Affiliation:
Université Paris 12 and Univ. P. & M. Curie
*
∗ Postal address: (1) LAGA URA 742, Institut Galilée Université Paris 13, Avenue J.B. Clément, 93430 Villetaneuse, France, (2) CERMICS, ENPC, 6-8 av. Blaise Pascal, cité Descartes, Champs-sur-Marne, F-77455 Marne-la-vallée Cedex 2, France. Email address: mba@erato.enpc.fr
∗∗ Postal address: (1) Dept. Math., Univ. Paris 12, Fac. Sciences, 61 av. Général de Gaulle, F-94010 Créteil Cedex, France, (2) Labo. Probabilités, URA 224, Univ. P. & M. Curie, Tour 56, 4, pl. Jussieu, F-75252 Paris Cedex 05, France. Email address: gpa@ccr.jussieu.fr
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Abstract

The shift method consists in computing the expectation of an integrable functional F defined on the probability space ((ℝd )N , B(ℝd )⊗N , μ⊗N ) (μ is a probability measure on ℝd ) using Birkhoff's Pointwise Ergodic Theorem, i.e.

as n → ∞, where θ denotes the canonical shift operator. When F lies in L 2(F T , μ⊗N ) for some integrable enough stopping time T, several weak (CLT) or strong (Gàl-Koksma Theorem or LIL) converging rates hold. The method successfully competes with Monte Carlo. The aim of this paper is to extend these results to more general probability distributions P on ((ℝd )N , B(ℝd )⊗N ), namely when the canonical process (X n )n∊N is P-stationary, α-mixing and fulfils Ibragimov's assumption

for some δ > 0. One application is the computation of the expectation of functionals of an α-mixing Markov Chain, under its stationary distribution P ν. It may both provide a better accuracy and save the random number generator compared to the usual Monte Carlo or shift methods on independent innovations.

Keywords

α-mixing processMonte Carlo methodrate of convergence

MSC classification

Primary:60F05: Central limit and other weak theorems60F15: Strong theorems60F17: Functional limit theorems; invariance principles

Information

Type
General Applied Probability
Information
Advances in Applied Probability , Volume 30 , Issue 2 , June 1998, pp. 425 - 448
Copyright
Copyright © Applied Probability Trust 1998 

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