Convergence Rates in the Law of Large Numbers for Arrays of Banach Valued Martingale Differences

Abstract

We study the convergence rates in the law of large numbers for arrays of Banach valued martingale differences. Under a simple moment condition, we show sufficient conditions about the complete convergence for arrays of Banach valued martingale differences; we also give a criterion about the convergence for arrays of Banach valued martingale differences. In the special case where the array of Banach valued martingale differences is the sequence of independent and identically distributed real valued random variables, our result contains the theorems of Hsu-Robbins-Erdös (1947, 1949, and 1950), Spitzer (1956), and Baum and Katz (1965). In the real valued single martingale case, it generalizes the results of Alsmeyer (1990). The consideration of Banach valued martingale arrays (rather than a Banach valued single martingale) makes the results very adapted in the study of weighted sums of identically distributed Banach valued random variables, for which we prove new theorems about the rates of convergence in the law of large numbers. The results are established in a more general setting for sums of infinite many Banach valued martingale differences. The obtained results improve and extend those of Ghosal and Chandra (1998).

Deviation inequalities for Banach space valued martingales differences sequences and random fields

“…In [HL14], Baum-Katz type estimates have been formulated for martingales differences arrays, extending the results in [Als90]. It has been extended to the Banach space valued setting in [Hao13]. However, it seems that our results cannot be compared with those of [HL14] because these ones require a control in of the L p -norm of…”