利用贝叶斯模型从部分信息预测多项式数据序列中的选举

IF 3.4 3区 经济学 Q1 ECONOMICS
Soudeep Deb, Rishideep Roy, Shubhabrata Das
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引用次数: 0

摘要

预测选举的获胜者对多方利益相关者都很重要。为了解决这个问题,我们考虑一个独立的分类数据序列,每个序列中可能出现的结果数量有限。假设数据是分批观察到的,每批数据都基于大量此类试验,并可通过多叉分布建模。我们假设类别的多项式概率随批次的不同而随机变化。我们面临的挑战是,如何根据截至几批的数据尽早对累积数据进行准确预测。在理论方面,我们首先推导出了多二叉单元概率估计值渐近正态性的充分条件,并提出了相应的适当变换。然后,在贝叶斯框架下,我们使用多元正态分布和反 Wishart 分布考虑分层先验,并建立后验收敛。利用这些结果和随之而来的吉布斯采样,就能得出所需的推论。我们用两个不同背景下的选举数据--一个来自印度,另一个来自美国--来演示该方法。通过模拟研究,我们对所提方法的有效性有了更深入的了解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting elections from partial information using a Bayesian model for a multinomial sequence of data

Predicting the winner of an election is of importance to multiple stakeholders. To formulate the problem, we consider an independent sequence of categorical data with a finite number of possible outcomes in each. The data is assumed to be observed in batches, each of which is based on a large number of such trials and can be modeled via multinomial distributions. We postulate that the multinomial probabilities of the categories vary randomly depending on batches. The challenge is to predict accurately on cumulative data based on data up to a few batches as early as possible. On the theoretical front, we first derive sufficient conditions of asymptotic normality of the estimates of the multinomial cell probabilities and present corresponding suitable transformations. Then, in a Bayesian framework, we consider hierarchical priors using multivariate normal and inverse Wishart distributions and establish the posterior convergence. The desired inference is arrived at using these results and ensuing Gibbs sampling. The methodology is demonstrated with election data from two different settings—one from India and the other from the United States. Additional insights of the effectiveness of the proposed methodology are attained through a simulation study.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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