Degree Type

Dissertation

Date of Award

2016

Degree Name

Doctor of Philosophy

Department

Statistics

Major

Statistics

First Advisor

Alicia L. Carriquiry

Second Advisor

Daniel J. Nordman

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.

DOI

https://doi.org/10.31274/etd-180810-5501

Copyright Owner

Guillermo Basulto-Elias

Language

en

File Format

application/pdf

File Size

86 pages

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