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Averaged Shifted Histograms: Effective Nonparametric Density Estimators in Several Dimensions
Open accessAbstract
We introduce two nonparametric multivariate density estimators that are particularly suitable for application in interactive computing environments. These estimators are statistically comparable to kernel methods and computationally comparable to histogram methods. Asymptotic theory of the estimators is presented and examples with univariate and simulated trivariate Gaussian data are illustrated.
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Cited by
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Snyder, L. H., Batista, A. P., & Andersen, R. A. (1997) · Nature · doi:10.1038/386167a0 · Cited by 1,103
Hyperdimensional Data Analysis Using Parallel Coordinates
Wegman, E. J. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10474926 · Cited by 688
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Shelton, T., Poorthuis, A., Graham, M., & Zook, M. (2014) · Geoforum · doi:10.1016/j.geoforum.2014.01.006 · Cited by 256
Metadata
- Authors
- Scott, D. W.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1985-09-01
- Volume
- 13
- Issue
- 3
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W1983993791
- Cited by
- 277
- References
- 0
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Scott, D. W. (1985). Averaged Shifted Histograms: Effective Nonparametric Density Estimators in Several Dimensions. The Annals of Statistics. https://doi.org/10.1214/aos/1176349654
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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