We introduce a class of local likelihood circular density estimators, which includes the kernel density estimator as a special case. The idea lies in optimizing a spatially weighted version of the log-likelihood function, where the logarithm of the density is locally approximated by a periodic polynomial. The use of von Mises density functions as weights reduces the computational burden. Also, we propose closed-form estimators which could form the basis of counterparts in the multidimensional Euclidean setting. Simulation results and a real data case study are used to evaluate the performance and illustrate the results.
Circular local likelihood / Marco Di Marzio, ; Stefania, Fensore; Agnese, Panzera; Taylor, Charles C.. - In: TEST. - ISSN 1133-0686. - STAMPA. - 27:(2018), pp. 921-945. [10.1007/s11749-017-0576-9]
Circular local likelihood
Agnese Panzera;
2018
Abstract
We introduce a class of local likelihood circular density estimators, which includes the kernel density estimator as a special case. The idea lies in optimizing a spatially weighted version of the log-likelihood function, where the logarithm of the density is locally approximated by a periodic polynomial. The use of von Mises density functions as weights reduces the computational burden. Also, we propose closed-form estimators which could form the basis of counterparts in the multidimensional Euclidean setting. Simulation results and a real data case study are used to evaluate the performance and illustrate the results.File | Dimensione | Formato | |
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