A Heavy-Tailed Multilevel Mixture Model for the Quick Count in the Mexican Elections of 2018

  • Michelle AnzarutEmail author
  • Luis Felipe González
  • María Teresa Ortiz
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 301)


Quick counts based on probabilistic samples are powerful methods for monitoring election processes. However, the complete designed samples are rarely collected to publish the results in a timely manner. Hence, the results are announced using partial samples, which have biases associated to the arrival pattern of the information. In this paper, we present a Bayesian hierarchical model to produce estimates for the Mexican gubernatorial elections. The model considers the poll stations poststratified by demographic, geographic, and other covariates. As a result, it provides a principled means of controlling for biases associated to such covariates. We compare methods through simulation exercises and apply our proposal in the July 2018 elections for governor in certain states. Our studies find the proposal to be more robust than the classical ratio estimator and other estimators that have been used for this purpose.


Bayesian calibration Hierarchical model Model-based inference Multilevel regression Poststratification Zero-inflated model 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michelle Anzarut
    • 1
    Email author
  • Luis Felipe González
    • 1
  • María Teresa Ortiz
    • 1
  1. 1.Instituto Tecnológico Autónomo de MéxicoAltavistaMexico

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