Abstract
Let $X, X_1, X_2, \cdots$ be independent random variables with $E(X_n) = 0 (n \geqq 1)$, and put $S_n = X_1 + \cdots + X_n (n \geqq 1)$. Marcinkiewicz and Zygmund [5] and Wiener [8] have shown that if the $X$'s have a common distribution, then \begin{equation*}\tag{1}E\{\sup_n|S_n/n|\} < \infty\end{equation*} provided that \begin{equation*}\tag{2}E\{|X|U (|X|)\} < \infty,\end{equation*} where we have put $U(x) = \max (1, \log x) (U_2(x) = U(U(x))$, etc.). Burkholder [2] has extended this result by showing that (1), (2), and \begin{equation*}\tag{3}E\{\sup_n|X_n/n|\} < \infty,\end{equation*} are equivalent. More recently, motivated by certain optimal stopping problems Teicher [7] and Bickel [1] under various assumptions on the distributions of $X_1, X_2, \cdots$ have shown that \begin{equation*}\tag{4}E\{\sup_n c_n|S_n|^\alpha\} < \infty\end{equation*} for certain sequences $(c_n)$ and positive constants $\alpha$. The interesting special case \begin{equation*}\tag{5}c_n = (nU_2(n))^{-\alpha/2}\end{equation*} is not covered by the results of these authors. This note gives a method which seems suitable for proving statements like (4) in a variety of cases. The method involves modifications of standard techniques used in the study of the law of the iterated logarithm. In particular, for each $\alpha = 1, 2, \cdots$ we are able to establish necessary and sufficient conditions for (4) when the $X$'s are identically distributed and the sequence $(c_n)$ satisfies (5). In Section 2 we state and prove one such theorem. Section 3 is devoted to explaining in somewhat more detail the scope of our results and their relation to the previously mentioned literature.
Citation
David Siegmund. "On Moments of the Maximum of Normed Partial Sums." Ann. Math. Statist. 40 (2) 527 - 531, April, 1969. https://doi.org/10.1214/aoms/1177697720
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