Classification of data observed with errors is a relevant topic which has still not been fully explored. We address this problem in the directional context and discuss two nonparametric approaches which rely on kernel estimation of circular densities. We provide some asymptotic L2 properties for the proposed methods, along with some simulation results.
Circular Kernel Classification with Errors in Variables / Marco Di Marzio; Stefania Fensore; Chiara Passamonti; Agnese Panzera. - STAMPA. - (2025), pp. 85-93. [10.1007/978-3-031-84702-8_10]
Circular Kernel Classification with Errors in Variables
Agnese Panzera
2025
Abstract
Classification of data observed with errors is a relevant topic which has still not been fully explored. We address this problem in the directional context and discuss two nonparametric approaches which rely on kernel estimation of circular densities. We provide some asymptotic L2 properties for the proposed methods, along with some simulation results.
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