Circular local likelihood

Marco Marzio (), Stefania Fensore (), Agnese Panzera () and Charles C. Taylor ()
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Marco Marzio: Università di Chieti-Pescara
Stefania Fensore: Università di Chieti-Pescara
Agnese Panzera: Università di Firenze
Charles C. Taylor: University of Leeds

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2018, vol. 27, issue 4, No 12, 945 pages

Abstract: 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.

Keywords: Bessel functions; Circular data; Density estimation; Log-likelihood; Numerical integration; Product kernels; von Mises density; 62G07 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11749-017-0576-9

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