Bayesian Models and Methods in Public Policy and Government Settings
Bayesian Models and Methods in Public Policy and Government Settings
复制标题
DOI：
10.1214/10-sts331
复制
发表时间：
2011-05-01
影响因子：
5.7
通讯作者：
Fienberg, Stephen E.
中科院分区：
文献类型：
--
作者：
Fienberg, Stephen E.
从20世纪50年代新贝叶斯的复兴开始，许多统计学家认为贝叶斯方法是不合适的，特别是在政府和公共政策设置中使用主观贝叶斯方法，因为它们依赖于先验分布。但贝叶斯框架通常提供了回答这些问题的主要方式，近年来贝叶斯应用的数量和多样性都有了显著增长。通过一系列历史和最近的例子，我们认为贝叶斯方法对先验和似然函数的正式和非正式评估被广泛接受，并应成为公共环境中的规范。我们的例子包括人口普查和小区域估计，美国大选之夜预测，向美国食品和药物管理局报告的研究，评估全球气候变化，以及衡量老年人残疾的潜在下降。
Starting with the neo-Bayesian revival of the 1950s, many statisticians argued that it was inappropriate to use Bayesian methods, and in particular subjective Bayesian methods in governmental and public policy settings because of their reliance upon prior distributions. But the Bayesian framework often provides the primary way to respond to questions raised in these settings and the numbers and diversity of Bayesian applications have grown dramatically in recent years. Through a series of examples, both historical and recent, we argue that Bayesian approaches with formal and informal assessments of priors AND likelihood functions are well accepted and should become the norm in public settings. Our examples include census-taking and small area estimation, US election night forecasting, studies reported to the US Food and Drug Administration, assessing global climate change, and measuring potential declines in disability among the elderly.