International Journal of Data Science and Analysis

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Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss

Received: 15 January 2020    Accepted: 04 February 2020    Published: 24 March 2020
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Abstract

There has been a growing interest by political pundits and scholars alike to predict the winner of the presidential elections. Although forecasting has now quite a history, we argue that the closeness of recent Kenyan presidential opinion polls and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the national wide pre-election polls prior to 2013 general elections and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into WinBUGS to determine the probability that a candidate will win an election. The model predicted the outright winner for the 2013 Kenyan election.

DOI 10.11648/j.ijdsa.20200601.17
Published in International Journal of Data Science and Analysis (Volume 6, Issue 1, February 2020)
Page(s) 58-63
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Presidential Elections, Election Forecasting, Operations Research, Bayesian Prediction Models

References
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Author Information
  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University Agriculture and Technology, Nairobi, Kenya

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  • APA Style

    Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu. (2020). Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss. International Journal of Data Science and Analysis, 6(1), 58-63. https://doi.org/10.11648/j.ijdsa.20200601.17

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    ACS Style

    Jeremiah Kiingati; Samuel Mwalili; Anthony Waititu. Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss. Int. J. Data Sci. Anal. 2020, 6(1), 58-63. doi: 10.11648/j.ijdsa.20200601.17

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    AMA Style

    Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu. Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss. Int J Data Sci Anal. 2020;6(1):58-63. doi: 10.11648/j.ijdsa.20200601.17

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  • @article{10.11648/j.ijdsa.20200601.17,
      author = {Jeremiah Kiingati and Samuel Mwalili and Anthony Waititu},
      title = {Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss},
      journal = {International Journal of Data Science and Analysis},
      volume = {6},
      number = {1},
      pages = {58-63},
      doi = {10.11648/j.ijdsa.20200601.17},
      url = {https://doi.org/10.11648/j.ijdsa.20200601.17},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijdsa.20200601.17},
      abstract = {There has been a growing interest by political pundits and scholars alike to predict the winner of the presidential elections. Although forecasting has now quite a history, we argue that the closeness of recent Kenyan presidential opinion polls and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the national wide pre-election polls prior to 2013 general elections and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into WinBUGS to determine the probability that a candidate will win an election. The model predicted the outright winner for the 2013 Kenyan election.},
     year = {2020}
    }
    

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    AU  - Jeremiah Kiingati
    AU  - Samuel Mwalili
    AU  - Anthony Waititu
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    AB  - There has been a growing interest by political pundits and scholars alike to predict the winner of the presidential elections. Although forecasting has now quite a history, we argue that the closeness of recent Kenyan presidential opinion polls and the wide accessibility of data should change how presidential election forecasting is conducted. We present a Bayesian forecasting model that concentrates on the national wide pre-election polls prior to 2013 general elections and considers finer details such as third-party candidates and self-proclaimed undecided voters. We incorporate our estimators into WinBUGS to determine the probability that a candidate will win an election. The model predicted the outright winner for the 2013 Kenyan election.
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