On the deviation between the distributions of sums and maximum sums of IID random vectors

Summary For a sequence of independent and identically distributed random vectors, with finite moment of order less than or equal to the second, the rate at which the deviation between the distribution functions of the vectors of partial sums and maximums of partial sums is obtained both when the sample size is fixed and when it is random, satisfying certain regularity conditions. When the second moments exist the rate is of ordern ^−1/4 (in the fixed sample size case). Two applications are given, first, we compliment some recent work of Ahmad (1979,J. Multivariate Anal.,9, 214–222) on rates of convergence for the vector of maximum sums and second, we obtain rates of convergence of the concentration functions of maximum sums for both the fixed and random sample size cases.     

About the distance between random walkers on some graphs

We consider two or more simple symmetric walks on \(\mathbb {Z}^d\) and the 2-dimensional comb lattice, and in case of finite collision, we investigate the properties of the distance among the walkers.     

Convolution equivalence and distributions of random sums

A serious gap in the Proof of Pakes’s paper on the convolution equivalence of infinitely divisible distributions on the line is completely closed. It completes the real analytic approach to Sgibnev’s theorem. Then the convolution equivalence of random sums of IID random variables is discussed. Some of the results are applied to random walks and Lévy processes. In particular, results of Bertoin and Doney and of Korshunov on the distribution tail of the supremum of a random walk are improved. Finally, an extension of Rogozin’s theorem is proved.     

Limit distributions of sums of m-dependent Bernoulli random variables

Summary The set of limit distributions of row sums of a triangular array of Bernoulli random variables which is strictly stationary and m-dependent in each row is characterized. Necessary and sufficient conditions for the convergence of the row sums to a given limit distribution are found. The case of convergence to a Poisson distribution is given special attention.     

Algorithms for computing the distributions of sums of discrete random variables

We present algorithms for computing the probablity density function of the sum of two independent discrete random variables, along with an implementation of the algorithm in a computer algebra system. Some examples illustrate the utility of this algorithm.     

Approximations to the distributions of ordered distance random variables

Summary In the present note we give short proofs of asymptotic theorems for the distributions of extreme and intermediate ordered distance random variables. Moreover, a quick goodness-of-fit test is proposed which is based on a single intermediate ordered distance random variable.     

On closure properties of heavy-tailed distributions for random sums*

We investigate some closure properties of heavy-tailed distributions for random sums. We first consider the random sums with independent or some certain dependent long-tailed increments. We also consider the real-valued increments with dominatedly varying-tailed distributions under extended negative dependence and obtain two results on necessary and sufficient conditions or sufficient conditions for the closure on random sums.     

Superlarge deviations for sums of random variables with arithmetical super-exponential distributions

Local limit theorems are obtained for superlarge deviations of sums S(n) = ξ(1) + ... + ξ(n) of independent identically distributed random variables having an arithmetical distribution with the right-hand tail decreasing faster that that of a Gaussian law. The distribution of ξ has the form ?(ξ = k) = \(e^{ - k^\beta L(k)} \), where β > 2, k ∈ ? (? is the set of all integers), and L(t) is a slowly varying function as t → ∞ which satisfies some regularity conditions. These theorems describing an asymptotic behavior of the probabilities ?(S(n) = k) as k/n → ∞, complement the results on superlarge deviations in [4, 5].     

On the accuracy of the normal approximation to the distributions of Poisson random sums

The analogue of the Berry–Esseen inequality for Poisson random sums is improved in 4–11 times depending on the maximal order 2 < s ≤ 3 of the finite absolute moment of the distribution of random summands. For the case, when s = 3, under additional assumption concerning the smoothness of distribution of summands we also obtained in some sense unimprovable estimate consisting of two summands: the first summand being the noncentral Lyapunov fraction with the coefficient equal to the unimprovable asymptotically exact constant and the second one decreases faster. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Inequalities for moments of symmetric polynomial forms in random variables

In the last few years, interest in the properties of distributions of quadratic and polynomial forms in random variables has again grown. Translated fromStatisticheskie Metody Otsenivaniya i Proverki Gipotez, pp. 159–161, Perm, 1993.     

Integro-local and integral theorems for sums of random variables with semiexponential distributions

We obtain some integro-local and integral limit theorems for the sums S(n) = ξ(1) + ? + ξ(n) of independent random variables with general semiexponential distribution (i.e., a distribution whose right tail has the form $P(\xi \ge t) = e^{ - t^\beta L(t)} $ , where β ∈ (0, 1) and L(t) is a slowly varying function with some smoothness properties). These theorems describe the asymptotic behavior as x → ∞ of the probabilities P(S(n) ∈ [x, x + Δ)) and P(S(n) ≥ x) in the zone of normal deviations and all zones of large deviations of x: in the Cramér and intermediate zones, and also in the “extreme” zone where the distribution of S(n) is approximated by that of the maximal summand.