Nonparametric recursive estimation of the copula

Felix Camirand Lemyre and Geoffrey Decrouez

Statistics & Probability Letters, 2021, vol. 168, issue C

Abstract: This paper introduces two nonparametric recursive estimators of the copula. These estimators employ a recursive estimation of the quantile achieved using a stochastic approximation algorithm. Their asymptotic properties and numerical performance are investigated in the context of i.i.d. data.

Keywords: Asymptotic theory; Copula; Nonparametric estimation; Recursive methods; Stochastic approximation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715220302327
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:168:y:2021:i:c:s0167715220302327

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.2020.108929

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 ().