Hostname: page-component-6bf8c574d5-h6jzd Total loading time: 0.001 Render date: 2025-02-27T01:06:47.980Z Has data issue: false hasContentIssue false
  • English
  • Français

Some Remarks on the “Generalized Event Count” Distribution

Published online by Cambridge University Press:  04 January 2017

John Londregan
Get access Rights & Permissions [Opens in a new window]

Abstract

King (1989) presented the “Generalized Event Count” (GEC) model as a means of dealing with event count data when the analyst is unsure whether the data are “underdispersed” or “overdispersed.” Here I establish several useful properties of the GEC model and make some practical suggestions for estimation.

Type
Symposium on the Generalized Event Count Estimator
Information
Political Analysis , Volume 6 , 1996 , pp. 213 - 223
Copyright
Copyright © Society for Political Methodology 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hogg, Robert V., and Tanis, Elliot A. 1977. Probability and Statistical Inference. New York: Macmillan.Google Scholar
Katz, Leo. 1965. “Unified Treatment of a Broad Class of Discrete Probability Distributions.” In Classical and Contagious Discrete Distributions, edited by Patil, G. P. Calcutta: Statistical Publishing Society.Google Scholar
King, Gary. 1989. “Variance Specification in Event Count Models: From Restrictive Assumptions to a Generalized Estimator.” American Journal of Political Science 33: 762–84.Google Scholar
Winkelmann, R., Signorino, C., and King, G. 1994. “A Correction for the Underdispersed Event Count Probability Distribution.” Political Analysis 5: 215–28.Google Scholar