The uniformly strong consistency of kernel-type distribution estimator under asymptotically almost negatively associated samples

Shipeng Wu, Yi Wu, Wenzhi Yang and Xuejun Wang

Statistical Theory and Related Fields, 2025, vol. 9, issue 2, 124-140

Abstract: This paper studies the kernel-type distribution estimator based on asymptotically almost negatively associated (AANA, for short) samples. The rate of uniformly strong consistency is established under some mild conditions. As applications, the uniformly strong convergence rates of kernel-type density estimator and kernel-type hazard rate estimator are also obtained. Some Monte Carlo simulations are presented to illustrate the finite sample performance of the kernel method. Finally, a real data analysis of Alibaba stock returns data is used to illustrate the usefulness of the proposed methodology.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2025.2484980 (text/html)
Access to full text is restricted to subscribers.

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:taf:tstfxx:v:9:y:2025:i:2:p:124-140

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20

DOI: 10.1080/24754269.2025.2484980

Access Statistics for this article

Statistical Theory and Related Fields is currently edited by Zhao Wei

More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().