Moment inqualities and strong laws of large numbers are proved for random allocations of balls into boxes. Random broken lines and random step lines are constructed using partial sums of i.i.d. random variables that are modified by random allocations. Functional limit theorems for such random processes are obtained.
Summary General linear combinations of independent winnings in generalized \St~Petersburg games are interpreted as individual gains that result from pooling strategies of different cooperative players. A weak law of large numbers is proved for all such combinations, along with some almost sure results for the smallest and largest accumulation points, and a considerable body of earlier literature is fitted into this cooperative framework. Corresponding weak laws are also established, both conditionally and unconditionally, for random pooling strategies.
Authors:István Berkes, Wolfgang Müller, and Michel Weber
Let f(n) be a strongly additive complex-valued arithmetic function. Under mild conditions on f, we prove the following weighted strong law of large numbers: if X,X1,X2, … is any sequence of integrable i.i.d. random variables, then
Based on a stochastic extension of Karamata’s theory of slowly varying functions, necessary and sufficient conditions are
established for weak laws of large numbers for arbitrary linear combinations of independent and identically distributed nonnegative
random variables. The class of applicable distributions, herein described, extends beyond that for sample means, but even
for sample means our theory offers new results concerning the characterization of explicit norming sequences. The general
form of the latter characterization for linear combinations also yields a surprising new result in the theory of slow variation.
The well-known characterization indicated in the title involves the moving maximal dyadic averages of the sequence (Xk: k = 1, 2, …) of random variables in Probability Theory. In the present paper, we offer another characterization of the SLLN
which does not require to form any maximum. Instead, it involves only a specially selected sequence of moving averages. The
results are also extended for random fields (Xkℓ: k, ℓ = 1, 2, …).