Deviation results for sparse tables 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 Nagaev (Theory Probab Appl 14(1):51–64, 1969; Theory Probab Appl 14(2):193–208, 1969). Here we adapt the proofs therein to deal with conditioned sums of such variables and solve the open question in Gamboa et al. (Bernoulli 18(4):1341–1360, 2012). 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 Wu (Ann Probab 22(1):17–27, 1994).