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On optimal and data-based histograms
Abstract
In this paper the formula for the optimal histogram bin width is derived which asymptotically minimizes the integrated mean squared error. Monte Carlo methods are used to verify the usefulness of this formula for small samples. A data-based procedure for choosing the bin width parameter is proposed, which assumes a Gaussian reference standard and requires only the sample size and an estimate of the standard deviation. The sensitivity of the procedure is investigated using several probability models which violate the Gaussian assumption.
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Cited by
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CFHTLenS: combined probe cosmological model comparison using 2D weak gravitational lensing
Kilbinger, M., Fu, L., Heymans, C., Simpson, F., Benjamin, J., Erben, T., Harnois-Déraps, J., Hoekstra, H., Hildebrandt, H., Kitching, T. D., et al. (2013) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stt041 · Cited by 349
Metadata
- Authors
- SCOTT, D. W.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1979
- Volume
- 66
- Issue
- 3
- Pages
- 605-610
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W1982366717
- Cited by
- 1,819
- References
- 0
- FWCI
- 2.34
- Type
- journal article
- Language
- en
- Open access
- closed
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- SCOTT, D. W. (1979). On optimal and data-based histograms. Biometrika. https://doi.org/10.1093/biomet/66.3.605
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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.
