A Review of the Importance of the Bootstrap Method in Statistical Estimation
DOI:
https://doi.org/10.31185/bsj.Vol22.Iss43.1587Keywords:
Bootstrap, Confidence Interval, Resampling, Parameter Estimation, Computational Statistics.Abstract
A Confidence Interval (CI) serves as an interval estimate for a population parameter derived from sample data. Traditional CI estimation becomes challenging when dealing with complex statistics that require impractical multi-step mathematical derivations. To address these limitations, the bootstrap method employs a resampling approach, generating multiple simulated datasets from the original sample to estimate the empirical distribution of the parameter.
This paper provides a comprehensive review of bootstrap concepts and the construction of bootstrap confidence intervals. The study aims to:
1. Clarify the fundamental principles of bootstrap methodology and its associated confidence intervals.
2. Evaluate different methodological frameworks used to derive bootstrap confidence intervals.
The review covers several key techniques, including the normal interval method, percentile bootstrap, basic bootstrap, first-order normal approximation, bias-corrected (BC), bias-corrected and accelerated (BCa), and the bootstrap-t method.
The study concludes that bootstrap confidence intervals are powerful due to their distribution-free nature and flexibility across statistical models. Despite being computationally intensive, modern computing capabilities make bootstrap methods highly practical and widely applicable.
This study adopts a structured narrative review approach to synthesize existing literature on bootstrap confidence interval methods.
A systematic search was conducted across major academic databases, including Scopus, Web of Science, Google Scholar, and PubMed. Keywords used included: “bootstrap methods”, “confidence intervals”, “bootstrap-t”, “BCa”, and “resampling techniques”.
The review covers publications from 2000 to 2025, in addition to foundational studies such as Efron (1979) and Efron & Tibshirani (1993).
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