Support Vector Regression and Beta Distribution for Modeling Incumbent Party for Presidential Elections

Authors

C. R. Kikawa, Department of Mathematics and Statistics, Faculty of Health and Applied Sciences, Namibia University of Science and Technology, Brahms and Haydn Street, Private Bag 13388, Windhoek, Namibia\\Department of Economics and Statistics, Faculty of Economics and Management Sciences, Kabale University, P.o.Box 317, Kabale, UgandaFollow
M. N. Ngungu, Democracy, Governance and Service Delivery, Human Sciences Research Council, Private Bag 41, Pretoria 0001, South AfricaFollow
D. Ntirampeba, Department of Economics and Statistics, Faculty of Economics and Management Sciences, Kabale University, P.o.Box 317, Kabale, UgandaFollow
A. Ssematimba, Department of Mathematics, Faculty of Science, Gulu University, P.o.Box 166, Gulu, UgandaFollow

Author Country (or Countries)

Uganda

Abstract

The present paper aims to model, predict, and explain presidential election results using selected quarterly macroeconomic indicators, i.e., gross national product, consumer price index, unemployment rate and gross national product from 1994-2017. We also seek to provide predictions of presidential winner prior to the elections based on the beta distribution and the support vector regression (SVR) as prediction models.Two models are primarily built based on beta distribution and SVR. Due to the forecasting aspect, model performance focuses on one goodness-of-fit measure, i.e., the prediction error rather than the squared correlation coefficient R2 as it makes little sense in a practical regression perspective. The best model is the one with the least mean square error (MSE). In this effect it turns out that the SVR with kernel type encapsulated postscript eps radial has a mean square error of 0.006 on the test set and is a better model compared to the beta distribution model with a mean square error of 1.216. Thu, an accurate solution to prediction of presidential vote elections via SVR analysis is proposed.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/140420

Recommended Citation

R. Kikawa, C.; N. Ngungu, M.; Ntirampeba, D.; and Ssematimba, A. (2020) "Support Vector Regression and Beta Distribution for Modeling Incumbent Party for Presidential Elections," Applied Mathematics & Information Sciences: Vol. 14: Iss. 4, Article 20.
DOI: http://dx.doi.org/10.18576/amis/140420
Available at: https://digitalcommons.aaru.edu.jo/amis/vol14/iss4/20