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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akaiki BA (1954) An approximation to the density function. Ann Inst Stat Math 6:127–132
Bowman AW, Azzalini A (1997) Applied smoothing technique for data analysis: the kernel approach with s-plus illustration. Oxford University Press, Oxford
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
Cacoullos T (1966) Estimation of a multivariate density. Ann Inst Stat Math 18:178–189
Devroye L, Gjorfi L (1984) Nonparametric density estimation: the L1 view. Wiley, New York
Duong T, Hazeltom ML (2005) Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimator. J Multivar Anal 93:417–433
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
Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, New York
Hall P, Marron JS (1987) Choice of kernel order in density estimation. Ann Stat 16:161–173
Hardle W, Muller M, Sperlich S, Werwatz A (2004) Nonparamtric and semiparametric models. Springer, Berlin
Jarnicka J (2009) Multivariate kernel density estimation with a parametric support. Opuscula Math 29(1):41–55
Lambert CG, Harrington SE, Harvey CE, Glodjo A (1999) Efficient online non-parametric kernel density estimation. Algorithmica 25:37–57
Marron JS, Wand MP (1992) Exact mean integrated squared error. Ann Stat 20(2):712–736
Martinez WL, Martinez AR (2002) Computational statistics handbook with MATLAB. Chapman & Hall/CRC, Florida
Michailidis PD, Margritis KG (2013) Accelerating kernel density estimation on the GPU using the CUDA framework. Appl Math Sci 7(30):1447–1476
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
Parzen E (1962) On the estimation of a probability density function and the mode. Ann Math Stat 33(3):1056–1076
Rosenblatt M (1956) Remarks on some nonparametric estimate of a density function. Ann Math Stat 27(3):832–837
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
Scott DW (1992) Multivariate density estimation: theory practice and visualization. Wiley, New York
Silverman BW (1986) Density estimation for statistics and data analysis. Chapman & Hall, London
Wand MP, Jones MC (1995) Kernel smoothing. Chapman & Hall, London
Wu TJ, Chen CF, Chen HYA (2007) Variable bandwidth selector in multivariate kernel density estimation. Stat Probab Lett 77(4):462–467
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-94-017-7236-5_3
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-017-7235-8
Online ISBN: 978-94-017-7236-5
eBook Packages: EngineeringEngineering (R0)