Date of Award


Degree Type


Degree Name

Master of Science - Mathematical Sciences


Mathematics and Statistics

First Advisor

Robert Henderson

Second Advisor

Gregory Miller

Third Advisor

Jacob Turner

Fourth Advisor

Emiliano Giudici


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.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Bag Wt Example.xlsx (192 kB)
Sample Dataset

MasterThesisBootVAR.xlsx (8 kB)
Percentile Bootstrap Results



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