BIAS REDUCTION IN KERNEL ESTIMATOR OF DENSITY FUNCTION IN BOUNDARY REGION

• Authors: Baszczyńska Aleksandra
• Languages of publication: EN

Abstracts

EN

The properties of the classical kernel estimator of density function deteriorate when the support of density function is bounded. The use of classical form of kernel estimator causes the increase of the bias estimator, particularly in the so-called boundary region, close to end of support. It can also lead to undesirable situation where density function estimator has a different support than the density function. The paper presents selected bias reduction procedures, such as reflection method and its modification. An example is presented with an attempt to compare considered procedures.

Keywords

EN

kernel estimator; density function; bias reduction; reflection method

• Publisher: Katedra Ekonometrii i Statystyki, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
• Journal: Metody Ilościowe w Badaniach Ekonomicznych
• Year: 2015
• Volume: 16
• Issue: 1
• Pages: 7-16

Dates

published

2015

Contributors

author

Baszczyńska Aleksandra

• Department of Statistical Methods, University of Lodz

References

• Albers G. M. (2012) Boundary Estimation of Densities with Bounded Support, Swiss Federal Institute of Technology, Zurich, https://stat.ethz.ch/research/mas_theses/2012/Martina_Albers [18.06.2015]
• Domański C., Pekasiewicz D., Baszczyńska A., Witaszczyk A. (2014) Testy statystyczne w procesie podejmowania decyzji, Wydawnictwo Uniwersytetu Łódzkiego, Łódź.
• Jones M. C. (1993) Simple Boundary Correction for Kernel Density Estimation, Statistics and Computing, 3, pp. 135-146.
• Jones M. C., Foster P. J. (1996) A Simple Nonnegative Boundary Correction Method for Kernel Density Estimation, Statistica Sinica, 6, pp. 1005-1013.
• Karunamuni R. J., Alberts T. (2005) On Boundary Correction in Kernel Density Estimation, Statistical Methodology, 2, pp. 191-212.
• Kulczycki P. (2005) Estymatory jądrowe w analizie systemowej, 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.