The classical bootstrap has proven its usefulness in many areas of statistical inference. However, some shortcomings of this method are also known. Therefore, various bootstrap modifications and other resampling algorithms have been introduced, especially for real-valued data. Recently, bootstrap methods have become popular in statistical reasoning based on imprecise data often modeled by fuzzy numbers. One of the challenges faced there is to create bootstrap samples of fuzzy numbers which are similar to initial fuzzy samples but different in some way at the same time. These methods are implemented in FuzzyResampling package and applied in different statistical functions like single-sample or two-sample tests for the mean. Besides describing the aforementioned functions, some examples of their applications as well as numerical comparisons of the classical bootstrap with the new resampling algorithms are provided in this contribution.
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For attribution, please cite this work as
Romaniuk & Grzegorzewski, "Resampling Fuzzy Numbers with Statistical Applications: FuzzyResampling Package", The R Journal, 2023
BibTeX citation
@article{RJ-2023-036,
author = {Romaniuk, Maciej and Grzegorzewski, Przemysław},
title = {Resampling Fuzzy Numbers with Statistical Applications: FuzzyResampling Package},
journal = {The R Journal},
year = {2023},
note = {https://doi.org/10.32614/RJ-2023-036},
doi = {10.32614/RJ-2023-036},
volume = {15},
issue = {1},
issn = {2073-4859},
pages = {271-283}
}