Deviation results for sparse tables in hashing with linear probing

TL;DR

This paper analyzes the probability of large deviations in total displacement for sparse tables in hashing with linear probing, adapting heavy-tailed sum deviation techniques and solving an open problem.

Contribution

It extends deviation results to sparse tables in hashing with linear probing, handling heavy-tailed distributions and conditioned sums, and addresses an open question in the field.

Findings

Established deviations for total displacement in sparse tables

Adapted heavy-tailed sum deviation techniques to conditioned sums

Solved an open problem related to deviations in hashing with linear probing

Abstract

We consider the model of hashing with linear probing and we establish the moderate and large deviations for the total displacement in sparse tables. In this context, Weibull-like-tailed random variables appear. Deviations for sums of such heavy-tailed random variables are studied in \cite{Nagaev69-1,Nagaev69-2}. Here we adapt the proofs therein to deal with conditioned sums of such variables and solve the open question in \cite{TFC12}. By the way, we establish the deviations of the total displacement in full tables, which can be derived from the deviations of empirical processes of i.i.d.\ random variables established in \cite{Wu94}..