Circular local likelihood

• Marco Di Marzio ^[2] ; Stefania Fensore ^[2] ; Agnese Panzera ^[3] ; Charles C. Taylor ^[1]
  1. [1] University of Leeds

     University of Leeds

     Reino Unido

     
  2. [2] Università di Chieti-Pescara
  3. [3] Università di Firenze

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 27, Nº. 4, 2018, págs. 921-945
• Idioma: inglés
• DOI: 10.1007/s11749-017-0576-9
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.