Kernel deconvolution density estimation

Date
2016-01-01
Authors
Basulto-Elias, Guillermo
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Abstract

This dissertation is about kernel deconvolution density estimation (KDDE), which is nonparametric density estimation based on a sample contaminated with measurement error. It is separated in four parts. First we explore some methodological aspects of KDDE. In the following two parts we describe the computational challenges in KDDE and our statistical software for KDDE in R. Finally, we propose a simple bandwidth selection procedure that has good theoretical properties.

Description
Keywords
bandwidth, deconvolution, density, flat-top, kernel
Citation
Source