COMPUTER-ASSISTED CHOICE OF SMOOTHING PARAMETER IN KERNEL METHODS APPLIED IN ECONOMIC ANALYSES

• Authors: Aleksandra Baszczyńska
• Languages of publication: EN

Abstracts

EN

In the kernel method, it is necessary to determine the value of the smoothing parameter. Not without significance is the fact of using the objectivity in the selection of this parameter and a certain automation of the selection procedure, which is important especially for novice users of kernel methods in the process of statistical inference. In the paper some methods of choice of the smoothing parameter are presented with the results of the simulation study that indicate these methods of selecting the smoothing parameter as handy tool when kernel methods are used in economic analyses.

Keywords

EN

kernel method; smoothing parameter; Silverman’s practical rule; SiZer map

• Publisher: Katedra Ekonometrii i Statystyki, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
• Journal: Metody Ilościowe w Badaniach Ekonomicznych
• Year: 2014
• Volume: 15
• Issue: 2
• Pages: 37-46

Dates

published

2014

Contributors

author

Aleksandra Baszczyńska

• Department of Statistical Methods University of Lodz

References

• Baszczyńska A. (2014) Testing Significance of Peaks in Kernel Density Estimator by SiZer Map, Proceedings of the 8th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena, Foundation of the Cracow University of Economics, Kraków.
• Chaudhuri P., Marron J.S. (1999) SiZer for Exploration of Structure in Curves, Journal of the American Statistical Association, 94, 447, 807 – 823.
• Domański C., Pekasiewicz D., Baszczyńska A., Witaszczyk A. (2014) Testy statystyczne
• w procesie podejmowania decyzji, Wydawnictwo Uniwersytetu Łódzkiego, Łódź.
• Domański C., Pruska K. (2000) Nieklasyczne metody statystyczne, PWE, Warszawa.
• Gajek L., Kałuszka M. (1996) Wnioskowanie statystyczne. Modele i metody, Wydawnictwa Naukowo-Techniczne. Warszawa.
• Horová I., Koláček J., Zelinka J. (2012) Kernel Smoothing in MATLAB. Theory and Practice of Kernel Smoothing, World Scientific, New Jersey.
• Silverman B.W. (1996) Density Estimation for Statistics and Data Analysis, Chapman & Hall, London.
• Wand M. P., Jones M.C. (1995) Kernel Smoothing, Chapman & Hall, London.