Kernel regression for errors-in-variables problems in the circular domain
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 de...
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| Veröffentlicht in: | Statistical methods & applications 2023-10, Vol.32 (4), p.1217-1237 |
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| container_title | Statistical methods & applications |
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| creator | Di Marzio, Marco Fensore, Stefania Taylor, Charles C. |
| description | 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. |
| doi_str_mv | 10.1007/s10260-023-00687-0 |
| format | Article |
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| subjects | Asymptotic properties Chemistry and Earth Sciences Computer Science Economics Errors Estimators Finance Health Sciences Humanities Insurance Kernels Law Management Mathematics and Statistics Medicine Original Paper Physics Random variables Statistical Theory and Methods Statistics Statistics for Business Statistics for Engineering Statistics for Life Sciences Statistics for Social Sciences Weighting functions |
| title | Kernel regression for errors-in-variables problems in the circular domain |
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