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

Marco Di Marzio (), Stefania Fensore () and Charles C. Taylor ()
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Marco Di Marzio: DSFPEQ, University of Chieti-Pescara
Stefania Fensore: DSFPEQ, University of Chieti-Pescara
Charles C. Taylor: University of Leeds

Statistical Methods & Applications, 2023, vol. 32, issue 4, No 8, 1217-1237

Abstract: Abstract 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.

Keywords: Characteristic function; Deconvolution kernels; Fourier coefficients; Measurement errors; Wind direction; CO pollution (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10260-023-00687-0

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