We consider the problem of nonparametrically estimating a circular density from data contaminated by angular measurement errors. Specifically, we obtain a kernel-type estimator with weight functions that are reminiscent of deconvolution kernels. Here, differently from the Euclidean setting, discrete Fourier coefficients are involved rather than characteristic functions. We provide some simulation results along with a real data application.
Kernel Circular Deconvolution Density Estimation / Marco Di Marzio, Stefania Fensore, Agnese Panzera, Charles C. Taylor. - STAMPA. - (2020), pp. 183-191. [10.1007/978-3-030-57306-5_17]
Kernel Circular Deconvolution Density Estimation
Agnese Panzera;
2020
Abstract
We consider the problem of nonparametrically estimating a circular density from data contaminated by angular measurement errors. Specifically, we obtain a kernel-type estimator with weight functions that are reminiscent of deconvolution kernels. Here, differently from the Euclidean setting, discrete Fourier coefficients are involved rather than characteristic functions. We provide some simulation results along with a real data application.
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