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A finite mixture mixed proportion regression model for classification problems in longitudinal voting data.

Rosineide da Paz1, Jorge Luis Bazán2, Victor Hugo Lachos3

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This summary is machine-generated.

This study introduces a new statistical model for analyzing clustered proportion data over time, like voter preferences in different regions. The method helps identify distinct groups with similar electoral behaviors using a Bayesian approach.

Keywords:
Bayesian methodsL-Logistic mixed modelclassificationmixture model

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Area of Science:

  • Social Sciences
  • Political Science
  • Statistics

Background:

  • Continuous clustered proportion data are common in social and political sciences.
  • Analyzing voter proportions over time across different regions presents challenges due to regional variations.
  • Identifying groups with similar electoral behavior profiles is crucial for understanding political dynamics.

Purpose of the Study:

  • To propose a novel statistical model for analyzing continuous clustered proportion data.
  • To develop a finite mixture of a random effects regression model using the L-Logistic distribution.
  • To effectively identify and analyze clusters of similar electoral behavior over time.

Main Methods:

  • A finite mixture of a random effects regression model based on the L-Logistic distribution is proposed.
  • A Markov chain Monte Carlo (MCMC) algorithm is employed for Bayesian inference.
  • The method is applied to analyze presidential election voting proportions in municipalities over time.

Main Results:

  • The proposed model successfully analyzes clustered proportion data, revealing distinct groups with similar voting patterns.
  • The Bayesian approach with MCMC provides robust posterior distributions for model parameters.
  • The analysis identified clusters of municipalities based on electoral behavior at different levels of favorable votes.

Conclusions:

  • The developed finite mixture model offers a powerful tool for analyzing complex clustered proportion data in political science.
  • The method facilitates the identification of regional electoral behavior clusters, enhancing understanding of political trends.
  • This approach provides a robust Bayesian framework for analyzing time-series proportion data with clustering effects.