论文标题
通过结合条件逻辑回归和主观贝叶斯来预测竞争：奥斯卡金像奖案例研究
Predicting competitions by combining conditional logistic regression and subjective Bayes: An Academy Awards case study
论文作者
论文摘要
长期以来，预测选举，体育赛事，娱乐奖和其他比赛的结果。在这些领域的复杂性，尤其是在数据驱动的新闻领域，旨在为普通受众群体迅速发展，作为历史信息迅速气球的可用性，这种预测正在增长。提供统计方法来概率地预测竞争成果面临两个主要挑战。首先，必须采用适当的一般建模方法来为竞争对手分配概率。其次，建模框架必须能够容纳专家意见，这通常可以使用，但很难完全封装在典型的数据集中。我们通过有条件的逻辑回归/主观贝叶斯方法来克服这些挑战。为了说明该方法，我们重新分析了最近的Time.com文章中的数据，在该文章中，作者试图使用标准逻辑回归预测2019年最佳图片学院奖得主。为了吸引和教育广泛的读者，我们讨论了通过在线应用程序部署所提出方法的策略。
Predicting the outcome of elections, sporting events, entertainment awards, and other competitions has long captured the human imagination. Such prediction is growing in sophistication in these areas, especially in the rapidly growing field of data-driven journalism intended for a general audience as the availability of historical information rapidly balloons. Providing statistical methodology to probabilistically predict competition outcomes faces two main challenges. First, a suitably general modeling approach is necessary to assign probabilities to competitors. Second, the modeling framework must be able to accommodate expert opinion, which is usually available but difficult to fully encapsulate in typical data sets. We overcome these challenges with a combined conditional logistic regression/subjective Bayes approach. To illustrate the method, we re-analyze data from a recent Time.com piece in which the authors attempted to predict the 2019 Best Picture Academy Award winner using standard logistic regression. Towards engaging and educating a broad readership, we discuss strategies to deploy the proposed method via an online application.
