Skip to main content
Log in

Distributions for spherical data based on nonnegative trigonometric sums

  • Regular Article
  • Published:
Statistical Papers Aims and scope Submit manuscript

Abstract

A family of distributions for a random pair of angles that determine a point on the surface of a three-dimensional unit sphere (three-dimensional directions) is proposed. It is based on the use of nonnegative double trigonometric (Fourier) sums (series). Using this family of distributions, data that possess rotational symmetry, asymmetry or one or more modes can be modeled. In addition, the joint trigonometric moments are expressed in terms of the model parameters. An efficient Newton-like optimization algorithm on manifolds is developed to obtain the maximum likelihood estimates of the parameters. The proposed family is applied to two real data sets studied previously in the literature. The first data set is related to the measurements of magnetic remanence in samples of Precambrian volcanics in Australia and the second to the arrival directions of low mu showers of cosmic rays.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Absil PA, Mahony R, Sepulchre R (2008) Optimization algorithms on matrix manifolds. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Anderson GL, Figueiredo RJP (1980) An adaptive orthogonal-series estimator for probability density functions. Ann Stat 8:347–376

    Article  MATH  Google Scholar 

  • Banerjee A, Dhillon IS, Ghosh J, Sra S (2005) Clustering on the unit hypersphere using Fisher distributions. J Mach Learn Res 6:1345–1382

    MathSciNet  MATH  Google Scholar 

  • Devroye L, Györfi L (1985) Nonparametric density estimation: the \(L_1\) view. Wiley, New York

  • Fejér L (1915) Über trigonometrische polynome. J Reine Angew Math 146:53–82

    MATH  Google Scholar 

  • Fernández-Durán JJ (2004) Circular distributions based on nonnegative trigonometric sums. Biometrics 60:499–503

    Article  MathSciNet  MATH  Google Scholar 

  • Fernández-Durán JJ (2007) Models for circular–linear and circular–circular data constructed from circular distributions based on nonnegative trigonometric sums. Biometrics 63:579–585

    Article  MathSciNet  MATH  Google Scholar 

  • Fernández-Durán JJ, Gregorio-Domínguez MM (2010) Maximum likelihood estimation of nonnegative trigonometric sum models using a Newton-like algorithm on manifolds. Electron J Stat 4:1402–1410

    Article  MathSciNet  MATH  Google Scholar 

  • Fernández-Durán JJ, Gregorio-Domínguez MM (2012) CircNNTSR: an R package for the statistical analysis of circular data using nonnegative trigonometric sums (NNTS) models. R package version 2.0-0. http://CRAN.R-project.org/package=CircNNTSR. Last accessed 17 Jan 2012

  • Figueiredo A (2008) Two-way ANOVA for the Watson distribution defined on the hypersphere. Stat Pap 49:363–376

    Article  MathSciNet  MATH  Google Scholar 

  • Fisher NI, Lewis T, Embleton BJJ (1987) Statistical analysis of spherical data. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Fisher RA (1953) Dispersion on a sphere. Proc R Soc Lond A 217:295–305

    Article  MATH  Google Scholar 

  • Gajek L (1986) On improving density estimators which are not bona fide functions. Ann Stat 14:1612–1618

    Article  MathSciNet  MATH  Google Scholar 

  • Hall P (1981) On trigonometric series estimates of densities. Ann Stat 9:683–685

    Article  MATH  Google Scholar 

  • Hall P, Watson GS, Cabrera J (1987) Kernel density estimation with spherical data. Biometrika 74:751–762

    Article  MathSciNet  MATH  Google Scholar 

  • Hendriks H (1990) Nonparametric estimation of a probability density on a Riemannian manifold using Fourier expansions. Ann Stat 18:832–849

    Article  MathSciNet  MATH  Google Scholar 

  • Hendriks H (2003) Application of fast spherical Fourier transform to density estimation. J Multivar Anal 84:209–221

    Article  MathSciNet  MATH  Google Scholar 

  • Hornik K, Grün B (2011) movMF: mixtures of von Mises Fisher distributions. R package version 0.0-0. http://CRAN.R-project.org/package=moVMF. Last accessed 9 Dec 2011

  • Kent JT (1982) The Fisher–Bingham distribution on the sphere. J R Stat Soc B Stat Methodol 44:71–80

    MathSciNet  MATH  Google Scholar 

  • Kim S, SenGupta A (2012) A three-parameter generalized von Mises distribution. Stat Pap. doi:10.1007/s00362-012-0454-1

  • Kronmal R, Tarter M (1968) The estimation of probability densities and cumulatives by Fourier series methods. J Am Stat Assoc 63:925–952

    Article  MathSciNet  MATH  Google Scholar 

  • Mardia KV, Jupp PE (2000) Directional statistics. Wiley, Chichester

    MATH  Google Scholar 

  • Peel D, Whiten WJ, McLachlan GJ (2001) Fitting mixtures of Kent distributions to aid in joint set identification. J Am Stat Assoc 96:56–63

    Article  MathSciNet  Google Scholar 

  • R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/. Last accessed 20 Nov 2011

  • Ridout MS, Linkie M (2009) Estimating overlap of daily activity patterns from camera trap data. J Agric Biol Environ Stat 14(3):322–337

    Article  MathSciNet  Google Scholar 

  • Schmidt PW, Embleton BJJ (1985) Pre-folding and overprint magnetic signatures in Precambrian (2.9-2.7ga) igneous rocks from the Pilbara Craton and Hamersley Basin, N.W. Australia. J Geophys Res 90(B4):2967–2984.

    Google Scholar 

  • Toyoda Y, Suga K, Murakami K, Hasegawa H, Shibata S, Domingo V, Escobar I, Kamata K, Bradt H, Clark G, La Pointe M (1965) Studies of primary cosmic rays in the energy region \(10^{14}\) eV to \(10^{17}\) eV (Bolivian air shower joint experiment). In: Proceedings of the international conference on cosmic rays, vol 2, London, September, 1965. The Institute of Physics and the Physical Society, London, pp 708–711.

  • Upton GJG, Fingleton B (1989) Spatial data analysis by example, vol 2 (categorical and directional data). Wiley, New York

  • Watson GS (1983) Statistics on spheres. The University of Arkansas, lecture notes in the mathematical sciences, 6. Wiley, New York

Download references

Acknowledgments

The authors wish to thank the Asociación Mexicana de Cultura, A.C. for its support and the reviewers for their useful comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. J. Fernández-Durán.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fernández-Durán, J.J., Gregorio-Domínguez, M.M. Distributions for spherical data based on nonnegative trigonometric sums. Stat Papers 55, 983–1000 (2014). https://doi.org/10.1007/s00362-013-0547-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00362-013-0547-5

Keywords

Mathematics Subject Classification

Navigation