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On the Efficiency of Second-Order d-Dimensional Product Kernels

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Transactions on Engineering Technologies

Abstract

This study considers the efficiency of second-order product kernels. The univariate form wherein the product kernels are derived is the symmetric beta kernels. We develop a formula for the efficiency using the Epanechnikov kernel as the optimum kernel based on the fundamentals of the Asymptotic Mean Integrated Square Error (AMISE). The results reveal that the efficiency of the product kernels decreases as their dimension increases and that the product form of the univariate biweight kernel has the highest efficiency value among the beta kernels.

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References

  1. Akaiki BA (1954) An approximation to the density function. Ann Inst Stat Math 6:127–132

    Article  MathSciNet  Google Scholar 

  2. Bowman AW, Azzalini A (1997) Applied smoothing technique for data analysis: the kernel approach with s-plus illustration. Oxford University Press, Oxford

    MATH  Google Scholar 

  3. Brox T, Rosenhahn B, Kersting U, Cremers D (2006) Nonparametric density estimation for human pose tracking. Lect Notes Comput Sci Pattern Recognit 4174:546–555

    Article  Google Scholar 

  4. Cacoullos T (1966) Estimation of a multivariate density. Ann Inst Stat Math 18:178–189

    Article  MathSciNet  MATH  Google Scholar 

  5. Devroye L, Gjorfi L (1984) Nonparametric density estimation: the L1 view. Wiley, New York

    Google Scholar 

  6. Duong T, Hazeltom ML (2005) Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimator. J Multivar Anal 93:417–433

    Article  MATH  Google Scholar 

  7. Fix E, Hodges JL (1951) Discriminatory analysis–nonparametric discrimination: consistency properties. USAF School of Aviation Medicine, Randolf Field, Texas, Report Number 4, Project Number 21-49-004. http://www.dtic.mil/dtic/tr/fulltext/u2/a800276.pdf. Cited Feb 1951

  8. Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, New York

    MATH  Google Scholar 

  9. Hall P, Marron JS (1987) Choice of kernel order in density estimation. Ann Stat 16:161–173

    Article  MathSciNet  MATH  Google Scholar 

  10. Hardle W, Muller M, Sperlich S, Werwatz A (2004) Nonparamtric and semiparametric models. Springer, Berlin

    Book  MATH  Google Scholar 

  11. Jarnicka J (2009) Multivariate kernel density estimation with a parametric support. Opuscula Math 29(1):41–55

    Article  MathSciNet  MATH  Google Scholar 

  12. Lambert CG, Harrington SE, Harvey CE, Glodjo A (1999) Efficient online non-parametric kernel density estimation. Algorithmica 25:37–57

    Article  MathSciNet  MATH  Google Scholar 

  13. Marron JS, Wand MP (1992) Exact mean integrated squared error. Ann Stat 20(2):712–736

    Article  MathSciNet  MATH  Google Scholar 

  14. Martinez WL, Martinez AR (2002) Computational statistics handbook with MATLAB. Chapman & Hall/CRC, Florida

    MATH  Google Scholar 

  15. Michailidis PD, Margritis KG (2013) Accelerating kernel density estimation on the GPU using the CUDA framework. Appl Math Sci 7(30):1447–1476

    MathSciNet  Google Scholar 

  16. Oyegue FO, Ogbonmwan SM (2014) The efficiency of product multivariate kernels. Lecture notes in engineering and computer science. In: Proceedings of the world congress on engineering and computer sciences, WCECS 2014. San Francisco, pp 830–834 22–24 Oct 2014

    Google Scholar 

  17. Parzen E (1962) On the estimation of a probability density function and the mode. Ann Math Stat 33(3):1056–1076

    Article  MathSciNet  Google Scholar 

  18. Rosenblatt M (1956) Remarks on some nonparametric estimate of a density function. Ann Math Stat 27(3):832–837

    Article  MathSciNet  MATH  Google Scholar 

  19. Rosehahn B, Brox T, Weikert J (2007) Three dimensional shape knowledge for joint image segmentation and pose tracking. Int J Comput Vis 73(3):243–262

    Article  Google Scholar 

  20. Scott DW (1992) Multivariate density estimation: theory practice and visualization. Wiley, New York

    Book  MATH  Google Scholar 

  21. Silverman BW (1986) Density estimation for statistics and data analysis. Chapman & Hall, London

    Book  MATH  Google Scholar 

  22. Wand MP, Jones MC (1995) Kernel smoothing. Chapman & Hall, London

    Book  MATH  Google Scholar 

  23. Wu TJ, Chen CF, Chen HYA (2007) Variable bandwidth selector in multivariate kernel density estimation. Stat Probab Lett 77(4):462–467

    Article  MathSciNet  MATH  Google Scholar 

  24. Zheng Y, Jestes J, Phillips JM, Li F (2013) Quality and efficiency in kernel density estimation for large data. In: Proceedings of the ACM SIGMOD international conference on management of data. ACM, New York, pp 433–444

    Google Scholar 

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Correspondence to F. O. Oyegue .

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Oyegue, F.O., Ogbonmwan, S.M., Ekhosuehi, V.U. (2015). On the Efficiency of Second-Order d-Dimensional Product Kernels. In: Kim, H., Amouzegar, M., Ao, Sl. (eds) Transactions on Engineering Technologies. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7236-5_3

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  • DOI: https://doi.org/10.1007/978-94-017-7236-5_3

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  • Publisher Name: Springer, Dordrecht

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  • Online ISBN: 978-94-017-7236-5

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