We study the problem of estimating circular densities when sample data are affected by measurement errors. We propose a deconvolution approach involving lower bias kernel estimators which take the additional source of bias due to the presence of measurement errors into account. Some asymptotic properties are discussed, and numerical results are provided along with a real data case study.
Lower Bias Circular Density Estimation with Contaminated Data / Marco DiMarzio; Stefania Fensore; Agnese Panzera; Chiara Passamonti. - STAMPA. - III:(2025), pp. 599-604. (Intervento presentato al convegno SIS 2024) [10.1007/978-3-031-64431-3_99].
Lower Bias Circular Density Estimation with Contaminated Data
Agnese Panzera;
2025
Abstract
We study the problem of estimating circular densities when sample data are affected by measurement errors. We propose a deconvolution approach involving lower bias kernel estimators which take the additional source of bias due to the presence of measurement errors into account. Some asymptotic properties are discussed, and numerical results are provided along with a real data case study.
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