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The exponentiated weibull family: some properties and a flood data application
Abstract
The exponentiated Weibull family, a Weibull extension obtained by adding a second shape parameter, consists of regular distributions with bathtub shaped, unimodal and a broad variety of monotone hazard rates. It can be used for modeling lifetime data from reliability, survival and population studies, various extreme value data, and for constructing isotones of the tests of the composite hypothesis of exponentiality. The structural analysis of the family in this paper includes study of its skewness and kurtosis properties, density shapes and tail character, and the associated extreme value and extreme spacings distributions. Its usefulness in modeling extreme value data is illustrated using the floods of the Floyd River at James, Iowa.
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Citations
Cites
Logistic regression, survival analysis, and the Kaplan-Meier curve.
Efron, B. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478612 · Cited by 1,065
Cited by
The Exponentiated Type Distributions
Nadarajah, S., & Kotz, S. (2006) · Acta Applicandae Mathematicae · doi:10.1007/s10440-006-9055-0 · Cited by 326
Metadata
- Authors
- Mudholkar, G. S., & Hutson, A. D.
- Venue
- Communications in Statistics - Theory and Methods
- Publisher
- Informa UK Limited
- Published
- 1996-01
- Volume
- 25
- Issue
- 12
- Pages
- 3059-3083
- ISSN
- 0361-0926 (ISSN-L)
- 1532-415X
- OpenAlex
- W2024999418
- Cited by
- 235
- References
- 1
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mudholkar, G. S., & Hutson, A. D. (1996). The exponentiated weibull family: some properties and a flood data application. Communications in Statistics - Theory and Methods. https://doi.org/10.1080/03610929608831886
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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.
