Explaining Rare Events in International Relations

By Gary King, Langche Zeng. International Organization, 2001

833 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/069c2d76e5de42d35720f10d1f9daedf52e6c3ea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fabrizio Gilardi
2. Katharina Füglister
3. Joachim Wagner
4. Kosuke Imai
5. Leo Guelman
6. Esteban Lafuente
7. Y. Vaillant
8. Eric von Hippel

In Sociology and Political Science

1. Alex R. Piquero
2. J. O'neal
3. Håvard Hegre
4. Leo Guelman
5. L. Daigle
6. F. Cullen
7. Joachim Wagner
8. M. Mousseau

In Statistics and Probability

1. Kosuke Imai
2. Gary King
3. Olivia Lau
4. Fabrizio Gilardi
5. Katharina Füglister
6. Joachim Wagner
7. Leo Guelman
8. Esteban Lafuente

Citations in the corpus

• MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET (2013, background): “16 See McFadden (1973) and King and Zeng (2001) for a comprehensive analysis of the use of the prior probability in the logit model. calculated in order to maintain the overall average expected default rate.”
• Are nascent entrepreneurs ‘Jacks-of-all-trades’? A test of Lazear's theory of entrepreneurship with German data (2006, background): “Gary King and Langche Zeng ( 2001a , b ) recently developed a version of the logit model to compute unbiased estimates in a situation like this.”
• Knowledge networks from patent data: Methodological issues and research targets (2004, background): “For more methodological remarks on this strategy, which applies to all instances of “rare events” (of which citations are a chief example) see also King and Zeng (2001).”
• The Global Diffusion of Regulatory Agencies (2011, neutral): “See King and Zeng (2001a, 2001b) for examples using rare events estimation in international relations.”
• Managing the Unknowable (2015, methodology): “…the homeruns.5 Relogit is a modification of the ordinary logistic regression, but unlike logistic regression and other standard regression techniques, relogit produces unbiased estimates when one of the values of the dependent binary variable greatly outnumbers the other (King and Zeng, 2001).”
• Impacts of Personality Traits on Consumer Innovation Success (2016, methodology): “Stata: Release 14.”
• Do different institutional frameworks condition the influence of local fear of failure and entrepreneurial examples over entrepreneurial activity? (2007, background): “A second set of dummy varia b les are included to represent the socio-cultural traits upon which the study bases its research framework.”
• Sampling Bias and Class Imbalance in Maximum-likelihood Logistic Regression (2011, methodology): “(Cosslett 1981b; Imbens 1992; King and Zeng 2001).”