A note on the maximum probability of ultra log-concave distributions

Jakimiuk et al. (2024) have proved that, if $X$ is an ultra log-concave random variable with integral mean, then $$\max_n \mathbb{P}\{X=n\} \geq \max_n \mathbb{P} \{Z=n\}\,,$$ where $Z$ is a Poisson random variable with the parameter $\mathbb{E}[X]$. In this note, we show that this inequality does not always hold true when $X$ is ultra log-concave with $\mathbb{E}[X]>1$.

Publication Details

Journal
ArXiv.org
Published
2025-02-27
Primary Topic
Point processes and geometric inequalities
Type
article
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article

A note on the maximum probability of ultra log-concave distributions

ArXiv.org
Point processes and geometric inequalities
article

A note on the maximum probability of ultra log-concave distributions

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Abstract

Jakimiuk et al. (2024) have proved that, if $X$ is an ultra log-concave random variable with integral mean, then $$\max_n \mathbb{P}\{X=n\} \geq \max_n \mathbb{P} \{Z=n\}\,,$$ where $Z$ is a Poisson random variable with the parameter $\mathbb{E}[X]$. In this note, we show that this inequality does not always hold true when $X$ is ultra log-concave with $\mathbb{E}[X]>1$.

ArXiv.org
Reduced inequalities
Openalex Percentile: Top 39%
Point processes and geometric inequalities
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A note on the maximum probability of ultra log-concave distributions · ArXiv.org (2025) | TGRS Research Map | TGRS