Abstract
The goal of this work is to introduce nonparametric kernel methods for density and regression estimation for circular data, and illustrate their use by a brief simulation study and real data application. Apart from supplying practitioners with a license free and easy to run code for the use of these methods, our aim is also to provide solutions to practical problems that may be encountered in their application. The real data examples belong to the International Polar Year project, concerned with the environmental change in the polar regions.
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References
Bowman AW, Azzalini A (1997) Applied smoothing techniques for data snalysis. Oxford Science Publications, New York
Di Marzio M, Panzera A, Taylor CC (2009) Local polynomial regression for circular predictors. Stat Probab Lett 79: 2066–2075
Fan J, Gijbels I (1996) Local polynomial modelling and its applications. Chapman and Hall, London
Hall P, Watson GP, Cabrera J (1987) Kernel density estimation for spherical data. Biometrika 74: 751–762
Helmers R, Mangku IW, Zitikis R (2003) Consistent estimation of the intensity function of a cyclic Poisson process. J Multivar Anal 84: 19–39
Helmers R, Mangku IW, Zitikis R (2005) Statistical properties of a kernel type estimator of the intensity of a cyclic Poisson process. J Multivar Anal 52: 1–23
Jammalamadaka SR, SenGupta A (2001) Topics in circular statistics. World Scientific, Singapore
Jammalamadaka SR, Lund UJ (2006) The effect of wind direction on ozone levels: a case study. Environ Ecol Stat 13: 287–298
Mardia KV, Jupp PE (2000) Directional statistics. Wiley, New York
Pewsey A (2006) Modelling asymmetrically distributed circular data using the wrapped skew-normal distribution. Environ Ecol Stat 13: 257–269
R Development Core Team: (2011) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
SenGupta A, Ugwuowo FI (2006) Assymetric circular–linear multivariate regression models with applications to environmental data. Environ Ecol Stat 13: 299–309
Sheather SJ, Jones MC (1991) A reliable data-based bandwidth selection method for kernel density estimation. J R Stat Soc Ser B 53: 683–690
Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, London
Sommerville MC, Mukerjee S, Fox DL (1996) Estimating the wind direction of maximum air pollutant concentration. Environmetrics 7: 231–243
Taylor CC (2008) Automatic bandwidth selection for circular density estimation. Comput Stat Data Anal 52: 3493–3500
Wand MP, Jones MC (1995) Kernel methods. Chapman and Hall, London
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Oliveira, M., Crujeiras, R.M. & Rodríguez-Casal, A. Nonparametric circular methods for exploring environmental data. Environ Ecol Stat 20, 1–17 (2013). https://doi.org/10.1007/s10651-012-0203-6
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DOI: https://doi.org/10.1007/s10651-012-0203-6