A Bias Reducing Approach for Some Robust Estimators by Predicting Roughness in Case of Kernel Estimation

• Authors: N. Gündüz , C. Aydın , E. Başar
• Languages of publication: EN

Abstracts

EN

In the density estimation it is known that estimators are heavily biased. We applied a bias reducing approach to improve some quantile estimators for Weibull distribution having different parameter values and contamination level. In this study, we estimate the bias for any quantile value and obtained biased reduced smoothed distribution function by simulation study for random samples of size 40. Then, the mean square error of some robust quantile estimators and variances are obtained from biased reduced smoothed distribution function. Furthermore, we obtained sampling distribution of roughness and sampling distribution of estimated bias related some quantile estimators.

Keywords

EN

02.70.Rr; 02.50.-r

Discipline

• 02.50.-r: Probability theory, stochastic processes, and statistics(see also section 05 Statistical physics, thermodynamics, and nonlinear dynamical systems)
• 02.70.Rr: General statistical methods

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2016
• Volume: 130
• Issue: 1
• Pages: 422-427

Dates

published

2016-07

Contributors

author

N. Gündüz

• Gazi University, Faculty of Science, Statistics Department, Ankara, Turkey

author

C. Aydın

• Gazi University, Faculty of Science, Statistics Department, Ankara, Turkey

author

E. Başar

• Gazi University, Faculty of Science, Statistics Department, Ankara, Turkey

References

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Identifiers

DOI

10.12693/APhysPolA.130.422