Bayesian Trivariate Analysis of an Opinion Poll: With Application to the Kenyan Pollss
International Journal of Data Science and Analysis
Volume 6, Issue 1, February 2020, Pages: 58-63
Received: Jan. 15, 2020;
Accepted: Feb. 4, 2020;
Published: Mar. 24, 2020
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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
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.
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.
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