Local bandwidth selectors for deconvolution kernel density estimation

Authors:
Achilleas Achilleos;Aurore Delaigle
Affiliations:
Department of Mathematics, University of Bristol, Bristol, UK BS8 1TW;Department of Mathematics and Statistics, University of Melbourne, Melbourne, Australia 3010
Venue:
Statistics and Computing
Year:
2012

Citing 6
Cited 0

An improved data-based algorithm for choosing the window width when estimating the density at a point

Computational Statistics & Data Analysis
An assessment of finite sample performance of adaptive methods in density estimation

Computational Statistics & Data Analysis
Density testing in a contaminated sample

Journal of Multivariate Analysis
Frequent problems in calculating integrals and optimizing objective functions: a case study in density deconvolution

Statistics and Computing
Nonparametric density deconvolution by weighted kernel estimators

Statistics and Computing
Estimating smooth distribution function in the presence of heteroscedastic measurement errors

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider kernel density estimation when the observations are contaminated by measurement errors. It is well-known that the success of kernel estimators depends heavily on the choice of a smoothing parameter called the bandwidth. A number of data-driven bandwidth selectors exist, but they are all global. Such techniques are appropriate when the density is relatively simple, but local bandwidth selectors can be more attractive in more complex settings. We suggest several data-driven local bandwidth selectors and illustrate via simulations the significant improvement they can bring over a global bandwidth.