The Finite Sample Performance of Modified Adaptive Kernel Estimators for Probability Density Function

Abstract

It is well-known that the most popular probability density estimator is kernel density estimator in literature. Adaptive kernel density estimators are generally preferred for data with long tailed densities. In this paper, the adaptive kernel estimators for probability density function are studied. A modified adaptive kernel estimator is investigated. For finite sample performance comparisons, the root mean squared errors of the fixed and the adaptive kernel estimations are computed for simulated samples from various density distributions. The simulation results show that the modified adaptive kernel density estimators have better performance than the classical adaptive kernel density estimator.