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Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach

Received: 17 August 2020     Accepted: 5 September 2020     Published: 19 September 2020
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Abstract

In this paper we apply sequential Bayesian approach to compare the outcome of the presidential polls in Kenya. We use the previous polls to form the prior for the current polls. Even though several authors have used non-Bayesian models for countrywide polling data to forecast the outcome of the presidential race we propose a Bayesian approach in this case. As such the question of how to treat the previous and current pre-election polls data is inevitable. Some researchers consider only the most recent poll others Combine all previous polls up the present time and treat it as a single sample, weighting only by sample size, while others Combine all previous polls but adjust the sample size according to a weight function depending on the day the poll is taken. In this paper we apply a sequential Bayesian model (as an advancement of the latter which is time sensitive) where the previous measure is used as the prior of the current measure. Our concern is to model the proportion of votes between two candidates, incumbent and challenger. A Bayesian model of our binomial variable of interest will be applied sequentially to the Kenya opinion poll data sets in order to arrive at a posterior probability statement. The simulation results show that the eventual winner must lead consistently and constantly in at least 60% of the opinions polls. In addition, a candidate demonstrating high variability is more likely to lose the polls.

Published in International Journal of Data Science and Analysis (Volume 6, Issue 4)
DOI 10.11648/j.ijdsa.20200604.13
Page(s) 113-119
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), 2020. Published by Science Publishing Group

Keywords

Sequential Bayesian Analysis, Bernoulli Opinion Polls, Election Forecasting, Simulation-Approach

References
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Cite This Article
  • APA Style

    Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu. (2020). Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach. International Journal of Data Science and Analysis, 6(4), 113-119. https://doi.org/10.11648/j.ijdsa.20200604.13

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    ACS Style

    Jeremiah Kiingati; Samuel Mwalili; Anthony Waititu. Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach. Int. J. Data Sci. Anal. 2020, 6(4), 113-119. doi: 10.11648/j.ijdsa.20200604.13

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    AMA Style

    Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu. Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach. Int J Data Sci Anal. 2020;6(4):113-119. doi: 10.11648/j.ijdsa.20200604.13

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  • @article{10.11648/j.ijdsa.20200604.13,
      author = {Jeremiah Kiingati and Samuel Mwalili and Anthony Waititu},
      title = {Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach},
      journal = {International Journal of Data Science and Analysis},
      volume = {6},
      number = {4},
      pages = {113-119},
      doi = {10.11648/j.ijdsa.20200604.13},
      url = {https://doi.org/10.11648/j.ijdsa.20200604.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200604.13},
      abstract = {In this paper we apply sequential Bayesian approach to compare the outcome of the presidential polls in Kenya. We use the previous polls to form the prior for the current polls. Even though several authors have used non-Bayesian models for countrywide polling data to forecast the outcome of the presidential race we propose a Bayesian approach in this case. As such the question of how to treat the previous and current pre-election polls data is inevitable. Some researchers consider only the most recent poll others Combine all previous polls up the present time and treat it as a single sample, weighting only by sample size, while others Combine all previous polls but adjust the sample size according to a weight function depending on the day the poll is taken. In this paper we apply a sequential Bayesian model (as an advancement of the latter which is time sensitive) where the previous measure is used as the prior of the current measure. Our concern is to model the proportion of votes between two candidates, incumbent and challenger. A Bayesian model of our binomial variable of interest will be applied sequentially to the Kenya opinion poll data sets in order to arrive at a posterior probability statement. The simulation results show that the eventual winner must lead consistently and constantly in at least 60% of the opinions polls. In addition, a candidate demonstrating high variability is more likely to lose the polls.},
     year = {2020}
    }
    

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    T1  - Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach
    AU  - Jeremiah Kiingati
    AU  - Samuel Mwalili
    AU  - Anthony Waititu
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    N1  - https://doi.org/10.11648/j.ijdsa.20200604.13
    DO  - 10.11648/j.ijdsa.20200604.13
    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ijdsa.20200604.13
    AB  - In this paper we apply sequential Bayesian approach to compare the outcome of the presidential polls in Kenya. We use the previous polls to form the prior for the current polls. Even though several authors have used non-Bayesian models for countrywide polling data to forecast the outcome of the presidential race we propose a Bayesian approach in this case. As such the question of how to treat the previous and current pre-election polls data is inevitable. Some researchers consider only the most recent poll others Combine all previous polls up the present time and treat it as a single sample, weighting only by sample size, while others Combine all previous polls but adjust the sample size according to a weight function depending on the day the poll is taken. In this paper we apply a sequential Bayesian model (as an advancement of the latter which is time sensitive) where the previous measure is used as the prior of the current measure. Our concern is to model the proportion of votes between two candidates, incumbent and challenger. A Bayesian model of our binomial variable of interest will be applied sequentially to the Kenya opinion poll data sets in order to arrive at a posterior probability statement. The simulation results show that the eventual winner must lead consistently and constantly in at least 60% of the opinions polls. In addition, a candidate demonstrating high variability is more likely to lose the polls.
    VL  - 6
    IS  - 4
    ER  - 

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

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

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

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