2002
DOI: 10.1007/s004400100176
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A normal comparison inequality and its applications

Abstract: Let ξ = (ξ i , 1 ≤ i ≤ n) and η = (η i , 1 ≤ i ≤ n) be standard normal random variables with covariance matrices R 1 = (r 1 ij ) and R 0 = (r 0 ij ), respectively. Slepian's lemmais at least 1. In this paper an upper bound is given. The usefulness of the upper bound is justified with three concrete applications: (i) the new law of the iterated logarithm of Erdős and Révész, (ii) the probability that a random polynomial does not have a real zero and (iii) the random pursuit problem for fractional Brownian parti… Show more

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Cited by 78 publications
(70 citation statements)
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“…[184,185]. Rigorous lower and upper bounds for θ(2) have also been obtained, the best one being θ(2) ∈ (1/(4 √ 3), 1/4] proved in [187] and [188]. We also mention an explicit expression for Q 0 ([a, b], n) for d = 2 obtained in Ref.…”
Section: Connections With Random Polynomialsmentioning
confidence: 97%
“…[184,185]. Rigorous lower and upper bounds for θ(2) have also been obtained, the best one being θ(2) ∈ (1/(4 √ 3), 1/4] proved in [187] and [188]. We also mention an explicit expression for Q 0 ([a, b], n) for d = 2 obtained in Ref.…”
Section: Connections With Random Polynomialsmentioning
confidence: 97%
“…Theorem 2.15 (Li and Shao, 2002) Suppose that (X 1i , 1 ≤ i ≤ n) are standard normal random variables with covariance matrix…”
Section: The Centered Normal Density Function With Covariance Matrixmentioning
confidence: 99%
“…For the covariance r 4 , Li and Shao (2002) established that b := 2q(0) lies in (0.5, 1]. Simulations of Dembo et al (2002) suggest that b = 0.76 ± 0.03.…”
Section: Estimation Of the Persistence Exponentmentioning
confidence: 99%