Brunello Gabriel Hideki Vatanabe, Nakano Eduardo Yoshio
Department of Statistics, University of Brasília, Campus Darcy Ribeiro, Brasília-DF, Brazil.
PLoS One. 2015 Mar 18;10(3):e0116924. doi: 10.1371/journal.pone.0116924. eCollection 2015.
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.
多数决投票系统的民意调查通常会显示每位候选人的得票百分比估计值。然而，比例投票制并不一定能保证得票率最高的候选人当选。因此，多数决选举中使用的传统方法不能应用于比例选举。在此背景下，本文的目的是在考虑巴西议席分配制度的情况下，对比例选举进行贝叶斯推断。更具体地说，开发了一种方法来回答特定政党在众议院获得代表权的概率。在贝叶斯场景下使用蒙特卡罗模拟技术进行推断，并将所开发的方法应用于2010年巴西立法议会和联邦众议院议员选举的数据。还给出了一个绩效率来评估该方法的效率。计算和模拟使用免费的R统计软件进行。
