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A new family of power transformations to improve normality or symmetry
Abstract
We introduce a new power transformation family which is well defined on the whole real line and which is appropriate for reducing skewness and to approximate normality. It has properties similar to those of the Box–Cox transformation for positive variables. The large‐sample properties of the transformation are investigated in the contect of a single random sample.
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Cited by
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Bishara, A. J., & Hittner, J. B. (2012) · Psychological Methods · doi:10.1037/a0028087 · Cited by 766
Finding Optimal Normalizing Transformations via bestNormalize
Peterson, R. (2021) · The R Journal · doi:10.32614/rj-2021-041 · Cited by 478
Zwanenburg, A. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04391-8 · Cited by 306
A Machine Learning Approach to Predict Air Quality in California
Castelli, M., Clemente, F. M., Popovič, A., Silva, S., & Vanneschi, L. (2020) · Complexity · doi:10.1155/2020/8049504 · Cited by 298
Metadata
- Authors
- Yeo, I.-K.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-12-01
- Volume
- 87
- Issue
- 4
- Pages
- 954-959
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2082137964
- Cited by
- 1,702
- References
- 0
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yeo, I.-K. (2000). A new family of power transformations to improve normality or symmetry. Biometrika. https://doi.org/10.1093/biomet/87.4.954
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
