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Zero-inflated modelling for characterizing coverage errors of extracts from the US Census Bureau's Master Address File

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  • Derek S. Young
  • Andrew M. Raim
  • Nancy R. Johnson

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  • Derek S. Young & Andrew M. Raim & Nancy R. Johnson, 2017. "Zero-inflated modelling for characterizing coverage errors of extracts from the US Census Bureau's Master Address File," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 73-97, January.
    • Handle: RePEc:bla:jorssa:v:180:y:2017:i:1:p:73-97
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    File URL: http://hdl.handle.net/10.1111/rssa.12183
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    References listed on IDEAS

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    1. Brian Neelon & Pulak Ghosh & Patrick F. Loebs, 2013. "A spatial Poisson hurdle model for exploring geographic variation in emergency department visits," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 389-413, February.
    2. Garay, Aldo M. & Hashimoto, Elizabeth M. & Ortega, Edwin M.M. & Lachos, Víctor H., 2011. "On estimation and influence diagnostics for zero-inflated negative binomial regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1304-1318, March.
    3. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    4. Wilson, Paul, 2015. "The misuse of the Vuong test for non-nested models to test for zero-inflation," Economics Letters, Elsevier, vol. 127(C), pages 51-53.
    5. Karlis, Dimitris & Ntzoufras, Ioannis, 2005. "Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i10).
    6. Heinzl, Harald & Mittlbock, Martina, 2003. "Pseudo R-squared measures for Poisson regression models with over- or underdispersion," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 253-271, October.
    7. Virginia Recta & Murali Haran & James L. Rosenberger, 2012. "A two‐stage model for incidence and prevalence in point‐level spatial count data," Environmetrics, John Wiley & Sons, Ltd., vol. 23(2), pages 162-174, March.
    8. Martin Ridout & John Hinde & Clarice G. B. Demétrio, 2001. "A Score Test for Testing a Zero‐Inflated Poisson Regression Model Against Zero‐Inflated Negative Binomial Alternatives," Biometrics, The International Biometric Society, vol. 57(1), pages 219-223, March.
    9. Daniel B. Hall, 2000. "Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study," Biometrics, The International Biometric Society, vol. 56(4), pages 1030-1039, December.
    10. Wang, Peiming, 2003. "A bivariate zero-inflated negative binomial regression model for count data with excess zeros," Economics Letters, Elsevier, vol. 78(3), pages 373-378, March.
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    Cited by:

    1. James Gaboardi, 2020. "Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance," Working Papers 20-5, Center for Economic Studies, U.S. Census Bureau.
    2. Qiang Fu & Tian‐Yi Zhou & Xin Guo, 2021. "Modified Poisson regression analysis of grouped and right‐censored counts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1347-1367, October.
    3. James Gaboardi, 2020. "Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance," Working Papers 20-05, Center for Economic Studies, U.S. Census Bureau.
    4. Livio Finos & Fortunato Pesarin, 2020. "On zero-inflated permutation testing and some related problems," Statistical Papers, Springer, vol. 61(5), pages 2157-2174, October.

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