Tail Behavior of Randomly Weighted Sums
Rajat Subhra Hazra, Krishanu Maulik

TL;DR
This paper investigates the tail behavior and convergence of randomly weighted sums of heavy-tailed variables, providing new sufficient conditions involving slowly varying functions and Mellin transforms.
Contribution
It introduces novel conditions for tail probability control and convergence of weighted sums, extending prior results to broader settings with less restrictive moment assumptions.
Findings
Conditions on slowly varying functions influence tail behavior.
Convergence of sums depends on moment conditions of weights.
Mellin transform conditions are necessary for tail equivalence.
Abstract
Let be a sequence of identically distributed and pairwise asymptotically independent random variables with regularly varying tails and be a sequence of positive random variables independent of the sequence . We shall discuss the tail probabilities and almost sure convergence of (where ) and and provide some sufficient conditions motivated by Denisov and Zwart (2007) as alternatives to the usual moment conditions. In particular, we illustrate how the conditions on the slowly varying function involved in the tail probability of helps to control the tail behavior of the randomly weighted sums. Note that, the above results allow us to choose as independent and identically distributed positive…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
