On the tail behaviour of the distribution function of the maximum for the partial sums of a class of i.i.d. random variables

Abstract: We take an $L_1$-dense class of functions $\Cal F$ on a measurable space $(X,\Cal X)$ and a sequence of i.i.d. $X$-valued random variables $\xi_1,\dots,\xi_n$, and give a good estimate on the tail behaviour of $\sup\limits_{f\in\Cal F}\sum\limits_{j=1}<sup>nf(\xi_j)$ if the conditions $\sup\limits_{x\in X}|f(x)|\le1$, $Ef(\xi_1)=0$ and $Ef(\xi_1)<sup>2&lt;\sigma<sup>2$ with some $0\le\sigma\le1$ hold for all $f\in\Cal F$. Roughly speaking this estimate states that under some natural conditions the above considered supremum is not much larger than the worst element taking part in it. The proof heavily depends on the main result of paper~[3]. Here we have to deal with such a problem where the classical methods worked out to investigate the behaviour of Gaussian or almost Gaussian random variables do not work.}