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A Berry-Esseen bound for the lightbulb process

Part of:Distribution theoryApplicationsCombinatorial probabilityProbability theory on algebraic and topological structures

Published online by Cambridge University Press:  01 July 2016

Larry Goldstein and
Haimeng Zhang
Larry Goldstein*
Affiliation:
University of Southern California
Haimeng Zhang*
Affiliation:
Mississippi State University
*
∗ Postal address: Department of Mathematics, University of Southern California, 1042 West 36th Place, KAP 108, Los Angeles, CA 90089-2532, USA. Email address: larry@math.usc.edu
∗∗ Postal address: Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS 39762, USA.
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Abstract

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In the so-called lightbulb process, on days r = 1,…,n, out of n lightbulbs, all initially off, exactly r bulbs, selected uniformly and independent of the past, have their status changed from off to on, or vice versa. With X the number of bulbs on at the terminal time n, an even integer, and μ = n/2, σ2 = var(X), we have supz ∈ R | P((X - μ)/σ ≤ z) - P(Z ≤ z) | ≤ nΔ̅0/2σ2 + 1.64n/σ3 + 2/σ, where Z is a standard normal random variable and Δ̅0 = 1/2√n + 1/2n + e−n/2/3 for n ≥ 6, yielding a bound of order O(n −1/2) as n → ∞. A similar, though slightly larger bound, holds for odd n. The results are shown using a version of Stein's method for bounded, monotone size bias couplings. The argument for even n depends on the construction of a variable X s on the same space as X that has the X-size bias distribution, that is, which satisfies E[X g(X)] = μE[g(X s )] for all bounded continuous g, and for which there exists a B ≥ 0, in this case B = 2, such that X ≤ X s ≤ X + B almost surely. The argument for odd n is similar to that for even n, but one first couples X closely to V, a symmetrized version of X, for which a size bias coupling of V to V s can proceed as in the even case. In both the even and odd cases, the crucial calculation of the variance of a conditional expectation requires detailed information on the spectral decomposition of the lightbulb chain.

Keywords

Normal approximationtoggle switchStein's methodsize biasing

MSC classification

Primary:62E17: Approximations to distributions (nonasymptotic)60C05: Combinatorial probability
Secondary:60B15: Probability measures on groups or semigroups, Fourier transforms, factorization62P10: Applications to biology and medical sciences

Information

Type
General Applied Probability
Information
Advances in Applied Probability , Volume 43 , Issue 3 , September 2011, pp. 875 - 898
Copyright
Copyright © Applied Probability Trust 2011 

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