Abstract

Using recent results on the behavior of multiple Wiener-Itô integrals based on Stein's method, we prove Hsu-Robbins and Spitzer's theorems for sequences of correlated random variables related to the increments of the fractional Brownian motion.

Highlights

  • A famous result by Hsu and Robbins [7] says that if X1, X2, . . . is a sequence of independent identically distributed random variables with zero mean and finite variance and Sn := X1 +. . .+Xn, P |Sn| > ǫn < ∞n≥1 for every ǫ > 0

  • The purpose of this paper is to prove Hsu-Robbins and Spitzer’s theorems for sequences of correlated random variables, related to the increments of fractional Brownian motion, in the spirit of

  • We note that the techniques generally used in the literature to prove the Hsu-Robbins and Spitzer’s results are strongly related to the independence of the random variables X1, X2, . . . . In our case the variables are correlated

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Summary

Introduction

A famous result by Hsu and Robbins [7] says that if X1, X2, . . . is a sequence of independent identically distributed random variables with zero mean and finite variance and Sn := X1 +. . .+Xn, . The purpose of this paper is to prove Hsu-Robbins and Spitzer’s theorems for sequences of correlated random variables, related to the increments of fractional Brownian motion, in the spirit of [5] or [12]. We note that the techniques generally used in the literature to prove the Hsu-Robbins and Spitzer’s results are strongly related to the independence of the random variables X1, X2, . We need to estimate the difference between the tail probabilities of Zn(1), Zn(2) and the tail probabilities of their limits To this end, we will use the estimates obtained in [2], [10] via Malliavin calculus and we are able to prove that this difference converges to zero in all cases. Throughout the paper we will denote by c a generic strictly positive constant which may vary from line to line (and even on the same line)

Preliminaries
Spitzer’s theorem
Hsu-Robbins theorem for the variations of fractional Brownian motion
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