Applied Mathematics
Volume 5, Issue 11 (June 2014)
ISSN Print: 2152-7385 ISSN Online: 2152-7393
Google-based Impact Factor: 0.58 Citations
Some Improvement on Convergence Rates of Kernel Density Estimator ()
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ABSTRACT
In this paper two kernel density estimators are introduced and investigated. In order to reduce bias, we intuitively subtract an estimated bias term from ordinary kernel density estimator. The second proposed density estimator is a geometric extrapolation of the first bias reduced estimator. Theoretical properties such as bias, variance and mean squared error are investigated for both estimators. To observe their finite sample performance, a Monte Carlo simulation study based on small to moderately large samples is presented.
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