Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated 35 min ago
PreprintCC BY 4.0arXiv

Convex Order Comparisons for Sub-Gamma Random Variables

Abstract

Recent work has shown that sub-Gaussian random variables are dominated in convex order by a sharp multiple of a Gaussian. We study the analogous question for the sub-Gamma class underlying Bernstein's inequality, with the Laplace law as the majorant. Here the moment generating function is controlled by a Bernstein-type bound over a finite range of frequencies, rather than by a purely quadratic bound. We derive a variational formula for the optimal multiple, show that it is strictly larger than the natural scale σ∨ α, and prove that this value is sharp, being attained by an asymmetric two-point distribution. We then turn to the sub-exponential class, which has a quadratic bound as in the sub-Gaussian case, but only over a bounded range as in the sub-Gamma case. Interestingly, the sub-exponential class displays a different behavior: the optimal multiple is exactly σ∨ α, but is attained only when α≤ σ; when α> σ, the constant remains sharp, but equality cannot hold for any non-affine convex function. Both results rely on a sharp finite-range variant of the Kearns-Saul inequality, which is of independent interest.

Related

  1. Separation Theorems for Increasing Convex and Weak 3-Convex Orders on Real-Valued Random Variables
    Yuting Su, Meng Guan, Zhenfeng Zou +1
    arXivPreprintarXiv licenceFull textProbabilityarXiv
  2. Extending Pathwise Gradients to Discrete Random Variables via Finite-Order Relaxation
    Donghan He, Luhuan Wu
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  3. Optimal fiber comparisons for random tensors
    Ziqian Lin, Huangang Lu
    arXivPreprintarXiv licenceFull textProbabilityStatisticsarXiv
  4. A Khintchine type inequality for non-independent random variables
    Jingyu Huang, Wenxuan Tao
    arXivPreprintarXiv licenceFull textProbabilityarXiv
  5. Cost Comparisons for Random and Deterministic Scans in Component-Wise Markov Chains
    Youngwoo Kwon
    arXivPreprintarXiv licenceFull textStatisticsarXiv
BibTeX
@misc{khribch2026convex,
  title = {{Convex Order Comparisons for Sub-Gamma Random Variables}},
  author = {El Mahdi Khribch and Badr-Eddine Chérief-Abdellatif},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.02398},
  eprint = {2609.02398},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.02398},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Convex Order Comparisons for Sub-Gamma Random Variables
AU  - El Mahdi Khribch
AU  - Badr-Eddine Chérief-Abdellatif
PY  - 2026
DA  - 2026/09/02
PB  - arXiv
DO  - 10.48550/arxiv.2609.02398
UR  - https://arxiv.org/abs/2609.02398
AB  - Recent work has shown that sub-Gaussian random variables are dominated in convex order by a sharp multiple of a Gaussian. We study the analogous question for the sub-Gamma class underlying Bernstein's inequality, with the Laplace law as the majorant. Here the moment generating function is controlled by a Bernstein-type bound over a finite range of frequencies, rather than by a purely quadratic bound. We derive a variational formula for the optimal multiple, show that it is strictly larger than the natural scale σ∨ α, and prove that this value is sharp, being attained by an asymmetric two-point distribution. We then turn to the sub-exponential class, which has a quadratic bound as in the sub-Gaussian case, but only over a bounded range as in the sub-Gamma case. Interestingly, the sub-exponential class displays a different behavior: the optimal multiple is exactly σ∨ α, but is attained only when α≤ σ; when α> σ, the constant remains sharp, but equality cannot hold for any non-affine convex function. Both results rely on a sharp finite-range variant of the Kearns-Saul inequality, which is of independent interest.
ER  -