Work

Low-bias discrimination of circular data with measurement errors

Year: 2025

Type: article

Abstract: We study nonparametric discrimination among circular density populations when sample data are affected by measurement errors. Relatively little research seems to have been devoted to this topic. Notoriously, in these problems, a nonparametric method needs to account for an additional source of bias due to the presence of measurement errors, beyond the usual bias typical of local methods. In the described context of abundant bias, we propose a deconvolution approach involving lower bias kernel estimators. Some asymptotic properties are discussed, and numerical results are provided along with a real data case study.

Source: Advances in Data Analysis and Classification

DOI: 10.1007/s11634-025-00655-4

Authors: Marco Di Marzio, Stefania Fensore, Agnese Panzera, Chiara Passamonti

Institutions: University of Chieti-Pescara, University of Florence

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funder: Università degli Studi G. d'Annunzio Chieti - Pescara