BIAS CORRECTION AT END POINTS IN KERNEL DENSITY ESTIMATION

On Boundary Correction in Kernel Density Estimation

It is well known now that kernel density estimators are not consistent when estimating a density near the finite end points of the support of the density to be estimated. This is due to boundary effects that occur in nonparametric curve estimation problems. A number of proposals have been made in the kernel density estimation context with some success. As of yet there appears to be no single do...

Kernel Density Estimation with Ripley’s Circumferential Correction

Simple boundary correction for kernel density estimation

If a probability density function has bounded support, kernel density estimates often overspill the boundaries and are consequently especially biased at and near these edges. In this paper, we consider the alleviation of this boundary problem. A simple unified framework is provided which covers a number of straightforward methods and allows for their comparison: 'generalized jackknifing' genera...

Nonparametric multiplicative bias correction for kernel-type density estimation on the unit interval

We consider kernel-type methods for estimation of a density on [0, 1] which eschew explicit boundary correction. Our starting point is the successful implementation of beta kernel density estimators of Chen (1999). We propose and investigate two alternatives. For the first, we reverse the roles of estimation point x and datapoint Xi in each summand of the estimator. For the second, we provide k...

The Relative Improvement of Bias Reduction in Density Estimator Using Geometric Extrapolated Kernel

One of a nonparametric procedures used to estimate densities is kernel method. In this paper, in order to reduce bias of  kernel density estimation, methods such as usual kernel(UK), geometric extrapolation usual kernel(GEUK), a bias reduction kernel(BRK) and a geometric extrapolation bias reduction kernel(GEBRK) are introduced. Theoretical properties, including the selection of smoothness para...