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Extra‐Binomial Variation in Logistic Linear Models

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  • D. A. Williams

Abstract

The logistic‐linear model, and its maximum likelihood estimation by iterated reweighted least squares, can be simply modified to incorporate a component of extra‐binomial variation. The modifications are very easily effected if the GLIM program is used.

Suggested Citation

  • D. A. Williams, 1982. "Extra‐Binomial Variation in Logistic Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 144-148, June.
    • Handle: RePEc:bla:jorssc:v:31:y:1982:i:2:p:144-148
    DOI: 10.2307/2347977
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    1. Maryam Aghayerashti & Ehsan Bahrami Samani & Mojtaba Ganjali, 2023. "Bayesian Latent Variable Model of Mixed Correlated Rank and Beta-Binomial Responses with Missing Data for the International Statistical Literacy Project Poster Competition," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 210-250, May.
    2. Garrett M. Fitzmaurice & John H. Goldthorpe, 1997. "Adjusting for Overdispersion in an Analysis of Comparative Social Mobility," Sociological Methods & Research, , vol. 25(3), pages 267-283, February.
    3. Chris J. Lloyd, 2000. "Regression Models for Convex ROC Curves," Biometrics, The International Biometric Society, vol. 56(3), pages 862-867, September.
    4. Peter J. Hannan & David M. Murray, 1996. "Gauss or Bernoulli?," Evaluation Review, , vol. 20(3), pages 338-352, June.
    5. Francesca Dominici & Giovanni Parmigiani, 2001. "Bayesian Semiparametric Analysis of Developmental Toxicology Data," Biometrics, The International Biometric Society, vol. 57(1), pages 150-157, March.
    6. Peter Congdon, 1990. "Issues in the Analysis of Small Area Mortality," Urban Studies, Urban Studies Journal Limited, vol. 27(4), pages 519-536, August.
    7. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
    8. Anwer S. Ahmed & Minsup Song & Douglas E. Stevens, 2009. "Earnings characteristics and analysts’ differential interpretation of earnings announcements: An empirical analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 49(2), pages 223-246, June.
    9. Richard B. Davies & Robert Crouchley, 1986. "The Mover-Stayer Model," Sociological Methods & Research, , vol. 14(4), pages 356-380, May.
    10. You-Gan Wang, 1999. "Estimating Equations for Removal Data Analysis," Biometrics, The International Biometric Society, vol. 55(4), pages 1263-1268, December.
    11. Christel Faes & Marc Aerts & Saskia Litière & Estelle Méroc & Yves Van der Stede & Koen Mintiens, 2011. "Estimating herd prevalence on the basis of aggregate testing of animals," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 155-174, January.
    12. Erni Tri Astuti & Takashi Yanagawa, 2002. "Testing Trend for Count Data with Extra-Poisson Variability," Biometrics, The International Biometric Society, vol. 58(2), pages 398-402, June.
    13. Mabel Morales-Otero & Vicente Núñez-Antón, 2021. "Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates," Mathematics, MDPI, vol. 9(3), pages 1-33, January.
    14. Steve Leeds & Alan E. Gelfand, 1989. "Estimation for dirichlet mixed models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 36(2), pages 197-214, April.
    15. Paul D. Allison, 1987. "Introducing a Disturbance into Logit and Probit Regression Models," Sociological Methods & Research, , vol. 15(4), pages 355-374, May.

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