Kernel regression for errors-in-variables problems in the circular domain

Autor: Marco Di Marzio, Stefania Fensore, Charles C. Taylor
Rok vydání: 2023
Předmět:
Zdroj: Statistical Methods & Applications.
ISSN: 1613-981X
1618-2510
DOI: 10.1007/s10260-023-00687-0
Popis: We study the problem of estimating a regression function when the predictor and/or the response are circular random variables in the presence of measurement errors. We propose estimators whose weight functions are deconvolution kernels defined according to the nature of the involved variables. We derive the asymptotic properties of the proposed estimators and consider possible generalizations and extensions. We provide some simulation results and a real data case study to illustrate and compare the proposed methods.
Databáze: OpenAIRE
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