Abstract
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.
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Anzarut, M., González, L.F., Ortiz, M.T. (2019). A Heavy-Tailed Multilevel Mixture Model for the Quick Count in the Mexican Elections of 2018. In: Antoniano-Villalobos, I., Mena, R., Mendoza, M., Naranjo, L., Nieto-Barajas, L. (eds) Selected Contributions on Statistics and Data Science in Latin America. FNE 2018. Springer Proceedings in Mathematics & Statistics, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-31551-1_1
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DOI: https://doi.org/10.1007/978-3-030-31551-1_1
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