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A new class of random vector entropy estimators and its applications in testing statistical hypotheses
Abstract
This paper proposes a new class of estimators of an unknown entropy of random vector. Its asymptotic unbiasedness and consistency are proved. Further, this class of estimators is used to build both goodness-of-fit and independence tests based on sample entropy. A simulation study indicates that the test involving the proposed entropy estimate has higher power than other well-known competitors under heavy tailed alternatives which are frequently used in many financial applications.
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Cited by
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Garcia, V., Debreuve, E., & Barlaud, M. (2008) · 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563100 · Cited by 447
On the Kozachenko–Leonenko entropy estimator
Delattre, S., & Fournier, N. (2017) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2017.01.004 · Cited by 50
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Casari, P., Neasham, J., Gubnitsky, G., Eccher, D., & Diamant, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-29413-2 · Cited by 12
Metadata
- Authors
- Goria, M. N., Leonenko, N. N., Mergel, V. V., & Novi Inverardi, P. L.
- Venue
- Journal of Nonparametric Statistics
- Publisher
- Informa UK Limited
- Published
- 2005-04
- Volume
- 17
- Issue
- 3
- Pages
- 277-297
- ISSN
- 1026-7654 (ISSN-L)
- 1048-5252
- 1029-0311
- OpenAlex
- W2017823450
- Cited by
- 176
- References
- 0
- FWCI
- 7.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Goria, M. N., Leonenko, N. N., Mergel, V. V., & Novi Inverardi, P. L. (2005). A new class of random vector entropy estimators and its applications in testing statistical hypotheses. Journal of Nonparametric Statistics. https://doi.org/10.1080/104852504200026815
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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.
