On Using Bayesian Methods to Address Small Sample Problems
On Using Bayesian Methods to Address Small Sample Problems
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DOI：
10.1080/10705511.2016.1186549
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发表时间：
2016-09-01
影响因子：
6
通讯作者：
McNeish, Daniel
中科院分区：
文献类型：
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作者：
McNeish, Daniel
随着贝叶斯方法在可访问性和普及性方面的不断增长，越来越多的实证研究转向贝叶斯方法来建模小样本数据。贝叶斯方法不依赖于渐近性，这一特性在小样本环境中采用频率论方法时可能是一个障碍。虽然贝叶斯方法更适合于对小样本数据进行建模，但估计值对先验分布的规格非常敏感。如果不注意这一点，贝叶斯估计实际上可能比频率论方法更差，特别是在使用频率论小样本校正的情况下。我们展示了说明性的模拟和应用的例子，依赖于软件默认值或扩散先验与小样本可以产生更有偏见的估计比频率论的方法。我们讨论了需要满足的条件，如果研究人员要负责任地利用贝叶斯方法提供的小样本问题的优势，以及领先的小样本频率论方法。
As Bayesian methods continue to grow in accessibility and popularity, more empirical studies are turning to Bayesian methods to model small sample data. Bayesian methods do not rely on asympotics, a property that can be a hindrance when employing frequentist methods in small sample contexts. Although Bayesian methods are better equipped to model data with small sample sizes, estimates are highly sensitive to the specification of the prior distribution. If this aspect is not heeded, Bayesian estimates can actually be worse than frequentist methods, especially if frequentist small sample corrections are utilized. We show with illustrative simulations and applied examples that relying on software defaults or diffuse priors with small samples can yield more biased estimates than frequentist methods. We discuss conditions that need to be met if researchers want to responsibly harness the advantages that Bayesian methods offer for small sample problems as well as leading small sample frequentist methods.