Electronic Theses and Dissertations

Date

2026

Document Type

Thesis

Degree Name

Master of Science

Department

Mathematical Sciences

Committee Chair

Andrews Anum

Committee Member

Ebenezer George

Committee Member

Lih Deng

Committee Member

Majid Noroozi

Abstract

We investigate the bootstrap methodology and current applications. The bootstrap is a non-parametric resampling method that has universal applicability thanks to the statistical concepts of weak convergence, the law of large numbers, and the central limit theorem. Modern applications of bootstrap methodology have demonstrated time and time again that it is computationally efficient and robust. The earliest days of the bootstrap saw it validated by generating statistics, confidence intervals, and hypothesis tests. It is important to note the universal application and efficiency of the bootstrap, especially when researchers are tempted to utilize the more financially and computationally expensive techniques of AI.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to ProQuest/Clarivate.”

Notes

Open Access

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