Weighted moment inequalities for bessel processes

Assume that φ(·) is a non-negative, continuously differentiable weight function and φ′(·) is nondecreasing on [0,∞), and let 0<θ<1. For any stopping time τ of a Bessel process X=(Xt)t≥0 of dimension α≥1 and starting at zero such that E[τμ]<∞, where 1<μ<∞ and (1−1μ)−1θ≤1, we establish an upper estimate for E[φ(sup0≤t≤τXt)(sup0≤t≤τ|Xt2−αt|)θ]. Our result extends the special case φ≡1 and θ≡1 proved by Graversen and Peškir (Citation1998).

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Publication Details

Journal
Sequential Analysis
Published
2026-06-24
DOI
https://doi.org/10.1080/07474946.2026.2680293
Primary Topic
Random Matrices and Applications
Type
article
Field-Weighted Citation Impact
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article

Weighted moment inequalities for bessel processes

Cloud Makasu
Sequential Analysis
Random Matrices and Applications
article

Weighted moment inequalities for bessel processes

Cloud Makasu
article en

Abstract

Assume that φ(·) is a non-negative, continuously differentiable weight function and φ′(·) is nondecreasing on [0,∞), and let 0<θ<1. For any stopping time τ of a Bessel process X=(Xt)t≥0 of dimension α≥1 and starting at zero such that E[τμ]<∞, where 1<μ<∞ and (1−1μ)−1θ≤1, we establish an upper estimate for E[φ(sup0≤t≤τXt)(sup0≤t≤τ|Xt2−αt|)θ]. Our result extends the special case φ≡1 and θ≡1 proved by Graversen and Peškir (Citation1998).

Sequential Analysis
Mathys (Netherlands) (NL)
Openalex Percentile: Top 5%
Random Matrices and Applications
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Weighted moment inequalities for bessel processes — Cloud Makasu · Sequential Analysis (2026) | TGRS Research Map | TGRS