Dergiler / Hacettepe Journal of Mathematics and Statistics / 2018 / Cilt: 47 - Sayı: 2

Density estimation of circular data with Bernstein polynomials

Sayfa
273–286
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Finally our method is illustrated with a simulation study and a real data example.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:This paper introduces a new, non-parametric approach to the modeling of circular data, based on the use of Bernstein polynomial densities.
📊
Temel Bulgular & Veri:The model generalizes the standard Bernstein polynomial model to account for the specific characteristics of circular data.
💡
Sonuç & Katkı:Finally our method is illustrated with a simulation study and a real data example.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:circular data · non-parametric modeling · Bernstein polynomials

Abstract

This paper introduces a new, non-parametric approach to the modeling of circular data, based on the use of Bernstein polynomial densities. The model generalizes the standard Bernstein polynomial model to account for the specific characteristics of circular data. In particular, it is shown that the trigonometric moments of the proposed circular Bernstein polynomial distribution can all be derived in closed form. Secondly, we introduce an approach to circular Bernstein polynomial density estimation given a sample of data and examine the properties of this estimator. Finally our method is illustrated with a simulation study and a real data example.

🔖

Bu Makaleye Atıf Yap

WİPER, M. P., CARNİCERO, J. A., Ausín, M. C. (2022). Density estimation of circular data with Bernstein polynomials. Hacettepe Journal of Mathematics and Statistics, Cilt: 47 - Sayı: 2, 273–286. https://ajindex.com/articles/529883

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (20)

  1. Babu, G.J., Canty, A.J. and Chaubey, Y.P. (2002). Application of Bernstein polynomials for smooth estimation of a distribution and density function, J. Stat. Plan. Infer., 105, 377–392.
  2. Babu, G.J. and Chaubey, Y.P. (2006). Smooth estimation of a distribution and density function on a hypercube using Bernstein polynomials for dependent random vectors, Stat. Probabil. Lett., 76 959–969.
  3. Bai, Z.D., Radhakrishna Rao, C. and Zhao, L.C. (1988). Kernel estimators of density function of directional data, J. Multivariate Anal., 27, 24–39.
  4. Burgin, M. (2010). Continuity in Discrete Sets. Discussion paper Available at http://arxiv.org/abs/1002.0036v1
  5. Butzer, P.L. (1955). Summability of generalized Bernstein polynomials, Duke Math. J., 22, 617–622.
  6. Carnicer, J.M. and Peña, J.M. (1993). Shape preserving representations and optimality of the Bernstein basis. Adv. Comput. Math., 1, 173–196.
  7. Carnicero, J.A., Ausín, M.C. and Wiper, M.P. (2013). Non-parametric copulas for circularlinear and circular-circular data: an application to wind directions. Stoch. Env. Res. Risk A. 27, 1991–2002.
  8. Eddy, W.F. (1980). Optimum kernel estimators of the mode. Ann. Statist., 8, 870-882. Fernández-Durán, J.J. (2004). Circular distributions based on nonnegative trigonometric sums, Biometrics, 60, 499–503.
  9. Fisher, N.I. (1989).
  10. Smoothing a sample of circular data, J. Structural Geology, 11, 775–778.
  11. Gradshteyn, I.S. and Ryzhik, I.M. (2007).
  12. Table of Integrals, Series, and Products, 7’th ed., Academic Press:
  13. Burlington. Hall, P., Watson, G.S. and Cabrera, J. (1987). Kernel density estimation for spherical data, Biometrika, 74, 751–762.
  14. Kakizawa, Y. (2004). Bernstein polynomial probability density estimation, J. Nonparametr. Stat., 16, 709–729. Klemelä, J. (2000). Estimation of Densities and Derivatives of Densities with Directional Data, J. Multivariate Anal., 73, 18–40.
  15. Lorentz, G.G. (1997). Bernstein Polynomials, 2nd. ed., AMS Chelsea, New York. Mardia, K.V. and Jupp, P. E. (1999).
  16. Directional Statistics, Wiley, Chichester. Mooney A., Helms P.J. and Jolliffe I.T. (2003). Fitting mixtures of von Mises distributions: a case study involving sudden infant death syndrome, Comput. Stat. Data An., 41, 505–513.
  17. Petrone, S. (1999a).
  18. Random Bernstein polynomials, Scand. J. Stat., 26, 373–393.
  19. Petrone, S. (1999b). Bayesian density estimation using random Bernstein polynomials, Can. J. Stat., 27, 105–126.
  20. Petrone, S. and Wassermann, L. (2002). Consistency of Bernstein polynomial posteriors, J. R. Stat. So