Ranked Simulated Resampling: A More Efficient and Accurate Resampling Approximations for Bootstrap Inference

Authors

Hani Samawi, Georgia Southern University, Jiann-Ping Hsu College of Public HealthFollow
Ding-Geng Chen, University of North Carolina, Chapel HillFollow

Document Type

Article

Publication Date

7-1-2021

Publication Title

Journal of Statistical Computation and Simulation

DOI

10.1080/00949655.2021.1946065

ISSN

1563-5163

Abstract

Since its invention, Efron’s bootstrap resampling approach has changed all the aspects of statistical inference, which has become the default framework whenever the classical inference approaches are not feasible. This paper introduces a new, more accurate, and efficient resampling approach, namely, the ranked simulated resampling approach. We show that, analytically and computationally, it is more efficient and precise than Efron’s uniform bootstrap resampling approach. We provide simulation studies and real data applications to support the comparison between the ranked simulated resampling approach and Efron’s uniform bootstrap resampling approach.

Comments

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Recommended Citation

Samawi, Hani, Ding-Geng Chen. 2021. "Ranked Simulated Resampling: A More Efficient and Accurate Resampling Approximations for Bootstrap Inference." Journal of Statistical Computation and Simulation, 91 (18): 3709-3720: Taylor & Francis Online. doi: 10.1080/00949655.2021.1946065 source: https://www.tandfonline.com/doi/full/10.1080/00949655.2021.1946065
https://digitalcommons.georgiasouthern.edu/bee-facpubs/324

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