We present a new adaptive kernel density estimator based on linear diffusion processes.
The proposed estimator builds on existing ideas for adaptive smoothing by incorporating …

The existence of high speed, inexpensive computing has made it easy to look at data in
ways that were once impossible. Where once a data analyst was forced to make restrictive …

Kernel estimators using non-negative kernels are considered to estimate probability density
functions with compact supports. The kernels are chosen from a family of beta densities. The …

We consider estimating density functions which have support on [0,∞) using some gamma
probability densities as kernels to replace the fixed and symmetric kernel used in the …

Basin perspective hydrology and hydraulic water-related queries often demanding an
accurate estimation of flood exceedance probabilities or return periods for assessing …

This article proposes a fully nonparametric kernel method to account for observed covariates
in regression discontinuity designs (RDD), which may increase precision of treatment effect …

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 …

Documenting space-use patterns of animals is important for understanding their ecology
and providing for their conservation. The concept of territory refers to a defended area …

We consider asymmetric kernel density estimators and smoothed histograms when the
unknown probability density function f is defined on [0,+∞). Uniform weak consistency on …

We propose a new method of boundary correction for kernel density estimation. The
technique is a kind of generalized reflection method involving reflecting a transformation of …