Abstract
The aim of this contribution is to develop a method for a bandwidthmatrix choice for kernel estimate of the first partial derivatives of the unknown density.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chac´on, J.E., Duong, T., Wand, M.P.: Asymptotics for general multivariate kernel density derivative estimators, Research Online (2009)
Duong, T., Cowling, A., Koch, I., Wand, M.P.: Feature significance for multivariate kernel density estimation. Comput. Stat. Data Anal. 52, 4225–4242 (2008)
Horov´a, I., Kol´aˇcek, J., Vopatov´a, K.: Visualization and Bandwidth Matrix Choice. To appear in Commun. Stat. Theory (2010)
Scott, D.W.: Multivariate density estimation: Theory, practice, and visualization. Wiley, New York (1992)
Terrell, G.R.: The maximal smoothing principle in density estimation. J. Ame. Stat. Assoc. 85 470–477 (1990)
Vopatov´a, K., Horov´a, I., Kol´aˇcek, J.: Bandwidth Matrix Choice for Bivariate Kernel Density Derivative. Proceedings of the 25th International Workshop on Statistical Modelling (Glasgow, UK), 561–564 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Horová, I., Vopatová, K. (2011). Kernel Density Gradient Estimate. In: Ferraty, F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2736-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-7908-2736-1_27
Published:
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2735-4
Online ISBN: 978-3-7908-2736-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)