Date of Award
5-2018
Degree Type
Thesis
Degree Name
Master of Science - Mathematical Sciences
Department
Mathematics and Statistics
First Advisor
Robert Henderson
Second Advisor
Gregory Miller
Third Advisor
Jacob Turner
Fourth Advisor
Emiliano Giudici
Abstract
The bootstrap procedure is widely used in nonparametric statistics to generate an empirical sampling distribution from a given sample data set for a statistic of interest. Generally, the results are good for location parameters such as population mean, median, and even for estimating a population correlation. However, the results for a population variance, which is a spread parameter, are not as good due to the resampling nature of the bootstrap method. Bootstrap samples are constructed using sampling with replacement; consequently, groups of observations with zero variance manifest in these samples. As a result, a bootstrap variance estimator will carry a bias to the low side. This work will attempt to demonstrate the bias issue with simulations, as well as explore possible approaches to correct for any such bias. In addition, these approaches will be evaluated for more general performance through simulations.
Repository Citation
Nguyen, Nghia Trong, "Evaluation of Using the Bootstrap Procedure to Estimate the Population Variance" (2018). Electronic Theses and Dissertations. 157.
https://scholarworks.sfasu.edu/etds/157
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Sample Dataset
MasterThesisBootVAR.xlsx (8 kB)
Percentile Bootstrap Results
Included in
Applied Statistics Commons, Other Statistics and Probability Commons, Probability Commons, Statistical Methodology Commons, Statistical Theory Commons
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