Kernel deconvolution density estimation
Date
2016-01-01
Authors
Basulto-Elias, Guillermo
Major Professor
Advisor
Alicia L. Carriquiry
Daniel J. Nordman
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Organizational Unit
Statistics
As leaders in statistical research, collaboration, and education, the Department of Statistics at Iowa State University offers students an education like no other. We are committed to our mission of developing and applying statistical methods, and proud of our award-winning students and faculty.
Journal Issue
Is Version Of
Versions
Series
Department
Statistics
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.
Comments
Description
Keywords
Citation
Source
Subject Categories
Keywords
Copyright
Fri Jan 01 00:00:00 UTC 2016