article

A plug-in rule for bandwidth selection in circular density estimation

Authors:

R. M. Crujeiras,

A. RodríGuez-CasalAuthors Info & Claims

Computational Statistics & Data Analysis, Volume 56, Issue 12

Pages 3898 - 3908

https://doi.org/10.1016/j.csda.2012.05.021

Published: 01 December 2012 Publication History

Abstract

A new plug-in rule procedure for bandwidth selection in kernel circular density estimation is introduced. The performance of this proposal is checked throughout a simulation study considering a variety of circular distributions exhibiting multimodality, peakedness and/or skewness. The plug-in rule behavior is also compared with other existing bandwidth selectors. The method is illustrated with some classical datasets.

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Cited By

Ameijeiras-Alonso J(2024)A reliable data-based smoothing parameter selection method for circular kernel estimationStatistics and Computing10.1007/s11222-024-10384-x34:2Online publication date: 7-Feb-2024
https://dl.acm.org/doi/10.1007/s11222-024-10384-x
Zámečník SHorová IKatina SHasilová K(2024)An adaptive method for bandwidth selection in circular kernel density estimationComputational Statistics10.1007/s00180-023-01401-039:4(1709-1728)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00180-023-01401-0
Xu DWang Y(2023)Density estimation for toroidal data using semiparametric mixturesStatistics and Computing10.1007/s11222-023-10305-433:6Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1007/s11222-023-10305-4
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Copyright © Elsevier B.V. © 2012.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 01 December 2012

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Cited By

Ameijeiras-Alonso J(2024)A reliable data-based smoothing parameter selection method for circular kernel estimationStatistics and Computing10.1007/s11222-024-10384-x34:2Online publication date: 7-Feb-2024
https://dl.acm.org/doi/10.1007/s11222-024-10384-x
Zámečník SHorová IKatina SHasilová K(2024)An adaptive method for bandwidth selection in circular kernel density estimationComputational Statistics10.1007/s00180-023-01401-039:4(1709-1728)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00180-023-01401-0
Xu DWang Y(2023)Density estimation for toroidal data using semiparametric mixturesStatistics and Computing10.1007/s11222-023-10305-433:6Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1007/s11222-023-10305-4
Saavedra-Nieves PCrujeiras R(2022)Nonparametric estimation of directional highest density regionsAdvances in Data Analysis and Classification10.1007/s11634-021-00457-416:3(761-796)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11634-021-00457-4
Benlakhdar SRziza MThami R(2022)Statistical modeling of directional data using a robust hierarchical von mises distribution model: perspectives for wind energyComputational Statistics10.1007/s00180-021-01173-537:4(1599-1619)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s00180-021-01173-5
Benlakhdar SRziza MThami RLazaar MTabii YChrayah MAl Achhab M(2017)Directional Data Classification Using a Hierarchical Model of Von Mises DistributionProceedings of the 2nd international Conference on Big Data, Cloud and Applications10.1145/3090354.3090425(1-6)Online publication date: 29-Mar-2017
https://dl.acm.org/doi/10.1145/3090354.3090425
Nuñez-Antonio GGutiérrez-Peña E(2014)A Bayesian model for longitudinal circular data based on the projected normal distributionComputational Statistics & Data Analysis10.5555/2749482.274994071:C(506-519)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.5555/2749482.2749940

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