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Smallest-fit selection of random sizes under a sum constraint: weak convergence and moment comparisons

Published online by Cambridge University Press:  01 July 2016

Frans A. Boshuizen*
Affiliation:
ING Bank Amsterdam
Robert P. Kertz*
Affiliation:
Georgia Institute of Technology
*
Postal address: ING Bank Amsterdam, The Netherlands.
∗∗ Postal address: School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0160, USA.

Abstract

In this paper, in work strongly related with that of Coffman et al. [5], Bruss and Robertson [2], and Rhee and Talagrand [15], we focus our interest on an asymptotic distributional comparison between numbers of ‘smallest’ i.i.d. random variables selected by either on-line or off-line policies. Let X1,X2,… be a sequence of i.i.d. random variables with distribution function F(x), and let X1,n,…,Xn,n be the sequence of order statistics of X1,…,Xn. For a sequence (cn)n≥1 of positive constants, the smallest fit off-line counting random variable is defined by Ne(cn) := max {jn : X1,n + … + Xj,ncn}. The asymptotic joint distributional comparison is given between the off-line count Ne(cn) and on-line counts Nnτ for ‘good’ sequential (on-line) policies τ satisfying the sum constraint ∑j≥1XτjIjn) ≤ cn. Specifically, for such policies τ, under appropriate conditions on the distribution function F(x) and the constants (cn)n≥1, we find sequences of positive constants (Bn)n≥1, (Δn)n≥1 and (Δ'n)n≥1 such that

for some non-degenerate random variables W and W'. The major tools used in the paper are convergence of point processes to Poisson random measure and continuous mapping theorems, strong approximation results of the normalized empirical process by Brownian bridges, and some renewal theory.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 1999 

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