PREDICTING COMPETITIONS BY COMBINING CONDITIONAL LOGISTIC REGRESSION AND SUBJECTIVE BAYES: AN ACADEMY AWARDS CASE STUDY
成果类型：
Article
署名作者：
Franck, Christopher T.; Wilson, Christopher E.
署名单位：
Virginia Polytechnic Institute & State University
刊物名称：
ANNALS OF APPLIED STATISTICS
ISSN/ISSBN：
1932-6157
DOI：
10.1214/21-AOAS1464
发表日期：
2021
页码：
2083-2100
关键词：
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 reanalyze 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. Toward engaging and educating a broad readership, we discuss strategies to deploy the proposed method via an online application.