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This paper gives upper and lower bounds for moments of sums of independent random variables $\left(X_k\right)$ which satisfy the condition ${P\left(\right|X|}_k\ge t)=exp(-N_k\left(t\right))$, where $N_k$ are concave functions. As a consequence we obtain precise information about the tail probabilities of linear combinations of independent random variables for which $N\left(t\right)={\left|t\right|}^r$ for some fixed 0 < r ≤ 1. This complements work of Gluskin and Kwapień who have done the same for convex functions N.