Optimal non-asymptotic concentration of centered empirical relative entropy in the high-dimensional regime

Yanpeng Li and Boping Tian

Statistics & Probability Letters, 2023, vol. 197, issue C

Abstract: This document establishes the optimal non-asymptotic concentration of the Kullback–Leibler divergence between the empirical distribution and the true distribution around its mean in the regime of K≫n, where K and n are the alphabet size and sample size, respectively.

Keywords: Non-asymptotic; Kullback–Leibler divergence; Empirical distribution; Plug-in entropy estimator (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715223000275
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:197:y:2023:i:c:s0167715223000275

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2023.109803

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().