Graduation Semester and Year
2020
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Mathematics
Department
Mathematics
First Advisor
Shan Sun-Mitchell
Abstract
To estimate an unknown density when observed measurements are from the convolution model contaminated by additive measurement errors, Stefanski and Carroll (1990) proposed using Fourier inversion on the product of Fourier transform of a kernel function and the characteristic function of the error variable. One important element in constructing such a density estimator is the bandwidth. The goal of this research is to establish an optimal bandwidth so that the mean integrated squared error of the estimator is minimized. The bootstrap method is used to accomplish this goal. The simulation results show that the estimated optimal bandwidths provide adequate estimation to the unknown densities.
Keywords
Deconvolving kernel density estimator, Optimal bandwidth, Bootstrap
Disciplines
Mathematics | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Sosa, Souad, "OPTIMAL BANDWIDTH SELECTION FOR DECONVOLUTED KERNEL DENSITY ESTIMATION USING BOOTSTRAP METHOD" (2020). Mathematics Dissertations. 229.
https://mavmatrix.uta.edu/math_dissertations/229
Comments
Degree granted by The University of Texas at Arlington