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Inference for L-estimators of location using a bootstrap warping approach
1Department of Biostatistics & Bioinformatics, Roswell Park Cancer Institute, Buffalo, New York, USA.
Communications in Statistics: Simulation and Computation
|September 20, 2021
Summary
We introduce bootstrap warping, a new semi-parametric bootstrap method for statistical hypothesis testing. This efficient procedure demonstrates strong type I error control and increasing power with sample size.
Area of Science:
- Statistics
- Statistical inference
Background:
- Hypothesis testing is fundamental in statistical analysis.
- Existing methods like empirical likelihood and bootstrap tilting have limitations.
- Computational efficiency and parameter stability are key considerations.
Purpose of the Study:
- To propose a novel semi-parametric bootstrap procedure for hypothesis tests.
- To introduce 'bootstrap warping' as an efficient and stable method.
- To evaluate its performance in statistical function testing.
Main Methods:
- Developed a new semi-parametric bootstrap procedure termed 'bootstrap warping'.
- Procedure is motivated by empirical likelihood and bootstrap tilting.
- Designed for computational efficiency with a fixed parameter set.
Main Results:
- Demonstrated good type I error control for hypothesis tests.
- Showcased monotone power as a function of sample size.
- Indicated effectiveness with shift alternatives.
Conclusions:
- Bootstrap warping offers an efficient and reliable method for hypothesis testing.
- The procedure provides robust type I error control.
- Its performance characteristics make it suitable for various statistical applications.
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