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Logistic Regression in Rare Events Data
Open accessAbstract
We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (
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Cited by
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Lin, M., & Viswanathan, S. (2016) · Management Science · doi:10.1287/mnsc.2015.2206 · Cited by 678
Mishina, Y., Dykes, B. J., Block, E. S., & Pollock, T. G. (2010) · Academy of Management Journal · doi:10.5465/amj.2010.52814578 · Cited by 578
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Metadata
- Authors
- King, G., & Zeng, L.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2003
- Volume
- 8
- Issue
- 2
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W1745603050
- Cited by
- 349
- References
- 0
- FWCI
- 12.39
- Type
- journal article
- Language
- en
- Open access
- diamond
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- King, G., & Zeng, L. (2003). Logistic Regression in Rare Events Data. Journal of Statistical Software. https://doi.org/10.18637/jss.v008.i02
Data sources
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.
