Volume 6, Issue 1, February 2020, Page: 58-63
Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss
Jeremiah Kiingati, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University Agriculture and Technology, Nairobi, Kenya
Samuel Mwalili, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University Agriculture and Technology, Nairobi, Kenya
Anthony Waititu, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University Agriculture and Technology, Nairobi, Kenya
Received: Jan. 15, 2020;       Accepted: Feb. 4, 2020;       Published: Mar. 24, 2020
DOI: 10.11648/j.ijdsa.20200601.17      View  287      Downloads  95
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.
Keywords
Presidential Elections, Election Forecasting, Operations Research, Bayesian Prediction Models
To cite this article
Jeremiah Kiingati, Samuel Mwalili, Anthony Waititu, Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss, International Journal of Data Science and Analysis. Vol. 6, No. 1, 2020, pp. 58-63. doi: 10.11648/j.ijdsa.20200601.17
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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