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Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach
International Journal of Data Science and Analysis
Volume 6, Issue 4, August 2020, Pages: 113-119
Received: Aug. 17, 2020; Accepted: Sep. 5, 2020; Published: Sep. 19, 2020
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Authors
Jeremiah Kiingati, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Samuel Mwalili, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Waititu, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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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.
Keywords
Sequential Bayesian Analysis, Bernoulli Opinion Polls, Election Forecasting, Simulation-Approach
To cite this article
Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu, Sequential Bayesian Analysis of Bernoulli Opinion Polls; a Simulation-Based Approach, International Journal of Data Science and Analysis. Vol. 6, No. 4, 2020, pp. 113-119. doi: 10.11648/j.ijdsa.20200604.13
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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