On the estimation of the mode by orthogonal series

N. Saadi and S. Adjabi

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 24, 7828-7839

Abstract: This article addresses the problem of estimating the mode of a density function under non parametric conditions based on an orthogonal series. We give a rigorous, theoretical account of the estimator’s properties (bias, variance, mean square error, convergence of the bias, convergence of the variance, convergence of the mean square error, and convergence in probability). Our results show that some of the theoretical properties of the orthogonal series’s estimator are similar to those of Parzen’s estimator. For example, under the condition that the density has two bounded derivatives in a neighborhood of the mode and the density is uniformly continuous. A simulation is used in order to study the behavior of the density estimator and shows that the estimator is performant.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2025.2483294 (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:lstaxx:v:54:y:2025:i:24:p:7828-7839

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2025.2483294

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().