Kernel smoothing

Kernel smoothing in Matlab

The toolbox for kernel estimation of curves in MATLAB (Kernel Smoothing Toolbox) was created. In the pack, there is a detailed help for the toolbox, too. You agree with licence conditions by downloading this software. You can download it here:

Unzip the downloaded file and copy to your working directory.

This toolbox is a part of the book:
Ivana Horová, Jan Koláček and Jiří Zelinka. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. World Scientific Publishing Co. Pte. Ltd., 2012. ISBN 978-981-4405-48-5.

Kernel estimation of the regression function

Some references:

[1] Jan Koláček. Kernel estimation of the regression function - bandwidth selection
Summer School DATASTAT'01 Proceedings FOLIA, p.129 - 138. 2001.
abstract
[2] Jan Koláček. Problems of automatic data-driven bandwidth selectors for nonparametric regression
Journal of Electrical Engineering, vol. 53, No. 12, p.48 - 52, SCAM'02 Bratislava. 2002.
abstract
[3] Jan Koláček. Use of Fourier transformation for kernel smoothing
Proceedings in Computational Statistics COMPSTAT'04, p.1329 - 1336. 2004
abstract
[4] J. Koláček, J. Poměnková. A Comparative Study of Boundary Effects for Kernel Smoothing
Austrian Journal of Statistics, 35/2006, p. 281-289. 2006.
abstract
[5] Jan Koláček. Plug-in method for nonparametric regression
Computational Statistics, Physica-Verlag, p. 63-78. 2008.
abstract
[6] Ivana Horová, Jan Koláček and Dagmar Lajdová. Kernel Regression Model for Total Ozone Data.
Journal of Environmental Statistics, 4/1, p. 1-12. 2013.
abstract

Kernel estimation of the density and the cumulative distribution function

The study is supported by research center "Jaroslav Hájek center for theoretical and applied statistics" (LC06024). The presentation of this project you can find here.

Some references:

[1] Jan Koláček. Boundary effects for densities and distribution functions.
Summer School DATASTAT'06 Proceedings FOLIA, p.141 - 148. 2006.
abstract
[2] I. Horová, J. Koláček, J. Zelinka, A.H. El-Shaarawi. Smooth Estimates of Distribution Functions with
Application in Environmental Studies.
Advanced topics on mathematical biology and ecology. WSEAS Press, p. 122-127. 2008.
abstract
[3] Jan Koláček. An Improved Estimator for Removing Boundary Bias in Kernel
Cumulative Distribution Function Estimation.
Proceedings in Computational Statistics COMPSTAT'08. Springer, p. 549-556. 2008.
abstract
[4] Jan Koláček, J.R. Karunamuni. A generalized reflection method for kernel distribution and hazard functions estimation.
Journal of Applied Probability and Statistics. Montgomery: Dixie W Publishing Corporation, 6, 2, p. 73-85. 2011.
abstract
[5] Ivana Horová, Jan Koláček and Kamila Vopatová Visualization and Bandwidth Matrix Choice.
Communications in Statistics - Theory and Methods, Philadelphia: Taylor & Francis, 41, 4, p. 759-777. 2012.
abstract
[6] Ivana Horová, Jan Koláček and Kamila Vopatová Full bandwidth matrix selectors for gradient kernel density estimate.
Computational Statistics & Data Analysis, ELSEVIER, 57, 1, p. 364-376, 2013.
abstract

ROC curves

The study is supported by research center "Jaroslav Hájek center for theoretical and applied statistics" (LC06024). The presentation of this project you can find here.

Some references:

[1] Jan Koláček. Boundary effects in kernel estimation of ROC curves.
Compstat 2006 Book of abstracts, p.158. 2006.
abstract
[2] J. Koláček, J.R. Karunamuni On boundary correction in kernel estimation of ROC curves.
Austrian Journal of Statistics, Vol. 38, No. 1, p. 17-32. 2009.
abstract
[3] J. Koláček, M. Řezáč, Assessment of Scoring Models Using Information Value.
19th International Conference on Computational Statistics, Paris France, August 22-27, 2010 Keynote, Invited and Contributed Papers.
1. vyd. Paris: SpringerLink, 2010. p. 1191-1198. 2010.
abstract
[4] M. Řezáč, J. Koláček Lift-Based Quality Indexes for Credit Scoring Models as an Alternative to Gini and KS.
Journal of Statistics: Advances in Theory and Applications, 7, 1, p. 1-23. 2012.
abstract