Displaying similar documents to “Limit theorems for randomly indexed sums of random vectors”

Strong Convergence for weighed sums of negatively superadditive dependent random variables

Zhiyong Chen, Haibin Wang, Xuejun Wang, Shuhe Hu (2016)

Kybernetika

Similarity:

In this paper, the strong law of large numbers for weighted sums of negatively superadditive dependent (NSD, in short) random variables is obtained, which generalizes and improves the corresponding one of Bai and Cheng ([2]) for independent and identically distributed random variables to the case of NSD random variables.

Gaussian Approximation of Moments of Sums of Independent Random Variables

Marcin Lis (2012)

Bulletin of the Polish Academy of Sciences. Mathematics

Similarity:

We continue the research of Latała on improving estimates of the pth moments of sums of independent random variables with logarithmically concave tails. We generalize some of his results in the case of 2 ≤ p ≤ 4 and present a combinatorial approach for even moments.

Asymptotics for Products of a Random Number of Partial Sums

Marcin Przystalski (2009)

Bulletin of the Polish Academy of Sciences. Mathematics

Similarity:

We consider products of a random number of partial sums of independent, identically distributed, positive square-integrable random variables. We show that the distribution of these products is asymptotically lognormal.

On asymptotic behaviors and convergence rates related to weak limiting distributions of geometric random sums

Tran Loc Hung, Phan Tri Kien, Nguyen Tan Nhut (2019)

Kybernetika

Similarity:

Geometric random sums arise in various applied problems like physics, biology, economics, risk processes, stochastic finance, queuing theory, reliability models, regenerative models, etc. Their asymptotic behaviors with convergence rates become a big subject of interest. The main purpose of this paper is to study the asymptotic behaviors of normalized geometric random sums of independent and identically distributed random variables via Gnedenko's Transfer Theorem. Moreover, using the...