One Value of Smoothing Parameter vs Interval of Smoothing Parameter Values in Kernel Density Estimation

Authors

  • Aleksandra Katarzyna Baszczyńska University of Łódź, Faculty of Economics and Sociology, Department of Statistical Methods

DOI:

https://doi.org/10.18778/0208-6018.332.05

Keywords:

kernel density estimation, smoothing parameter, ad hoc methods

Abstract

Ad hoc methods in the choice of smoothing parameter in kernel density estimation, al­though often used in practice due to their simplicity and hence the calculated efficiency, are char­acterized by quite big error. The value of the smoothing parameter chosen by Silverman method is close to optimal value only when the density function in population is the normal one. Therefore, this method is mainly used at the initial stage of determining a kernel estimator and can be used only as a starting point for further exploration of the smoothing parameter value. This paper pre­sents ad hoc methods for determining the smoothing parameter. Moreover, the interval of smooth­ing parameter values is proposed in the estimation of kernel density function. Basing on the results of simulation studies, the properties of smoothing parameter selection methods are discussed.

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References

Baszczyńska A. (2014). Computer-Assisted Choice of Smoothing Parameter in Kernel Methods Applied in Economic Analysis. Quantitative Methods in Economics (Metody Ilościowe w Badaniach Ekonomicznych). Warsaw University of Life Sciences Press. Warsaw. XV/2. 37-46.
Google Scholar

Baszczyńska A. (2016). Nonclassical Parameters in Kernel Estimation. Bulletin de la Société des Sciences et des Letters de Łódź. Recherches sur les Déformations. 1. LXVI. 2016. 135-148.
Google Scholar

Heidenreich N.. Schindler A.. Sperlich S. (2013). Bandwidth Selection for Kernel Density Estimation: a Review of Fully Automatic Selectors. AStA Advances in Statistical Analysis. 97. 4. 403–433.
Google Scholar

Horová I.. Koláček J.. Zelinka J. (2012). Kernel Smoothing in Matlab. Theory and Practice of Kernel Smoothing. World Scientific. New Jersey.
Google Scholar

Li Q.. Racine J. S. (2007). Nonparametric Econometrics. Theory and Practice. Princeton University Press. Princeton and Oxford.
Google Scholar

Kulczycki P. (2005). Estymatory jądrowe w analizie systemowej. Wydawnictwa Naukowo-Techniczne. Warszawa.
Google Scholar

Pekasiewicz D. (2015). Statystyki pozycyjne w procedurach estymacji i ich zastosowania w badaniach ekonomicznych. Wydawnictwo Uniwersytetu Łódzkiego. Łódź.
Google Scholar

Silverman B.W. (1996). Density Estimation for Statistics and Data Analysis. Chapman and Hall. London.
Google Scholar

Scott D. (2015). Multivariate Density Estimation. Theory, Practice, and Visualization. Wiley. Hoboken, New Jersey.
Google Scholar

Wand M. P.. Jones M.C. (1995). Kernel Smoothing. Chapman and Hall. London.
Google Scholar

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Published

2018-02-02

How to Cite

Baszczyńska, A. K. (2018). One Value of Smoothing Parameter vs Interval of Smoothing Parameter Values in Kernel Density Estimation. Acta Universitatis Lodziensis. Folia Oeconomica, 6(332), 73–86. https://doi.org/10.18778/0208-6018.332.05

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