结合条件逻辑回归与主观贝叶斯预测竞赛：一项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
AI总结：
本文提出结合条件逻辑回归与主观贝叶斯的方法预测竞赛结果，以2019年奥斯卡最佳影片奖为例验证其有效性，并探讨在线应用部署策略。
摘要：
预测选举、体育赛事、娱乐奖项及其他竞赛的结果长期以来一直激发着人类的想象。随着历史信息的可获取性迅速膨胀，这些领域的预测日益复杂，尤其是在面向普通受众的数据驱动新闻这一快速发展的领域中。为竞赛结果提供概率预测的统计方法面临两大主要挑战。首先，需要一种适当通用的建模方法来为竞争者分配概率。其次，建模框架必须能够容纳专家意见，而专家意见通常可以获得，但难以在典型数据集中被完全囊括。我们通过结合条件逻辑回归与主观贝叶斯的方法克服了这些挑战。为说明该方法，我们重新分析了Time.com近期一篇文章中的数据，该文作者曾尝试使用标准逻辑回归预测2019年奥斯卡最佳影片奖得主。为了吸引并教育广大读者，我们讨论了通过在线应用部署所提出方法的策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
