Farrell Simon, Ludwig Casimir J H
University of Bristol, Bristol, England.
Psychon Bull Rev. 2008 Dec;15(6):1209-17. doi: 10.3758/PBR.15.6.1209.
Hierarchical (or multilevel) statistical models have become increasingly popular in psychology in the last few years. In this article, we consider the application of multilevel modeling to the ex-Gaussian, a popular model of response times. We compare single-level and hierarchical methods for estimation of the parameters of ex-Gaussian distributions. In addition, for each approach, we compare maximum likelihood estimation with Bayesian estimation. A set of simulations and analyses of parameter recovery show that although all methods perform adequately well, hierarchical methods are better able to recover the parameters of the ex-Gaussian, by reducing variability in the recovered parameters. At each level, little overall difference was observed between the maximum likelihood and Bayesian methods.
在过去几年中，分层（或多级）统计模型在心理学领域越来越受欢迎。在本文中，我们考虑将多级建模应用于前高斯分布，这是一种常用的反应时间模型。我们比较了单级和分层方法来估计前高斯分布的参数。此外，对于每种方法，我们还比较了最大似然估计和贝叶斯估计。一组参数恢复的模拟和分析表明，尽管所有方法的表现都相当不错，但分层方法通过减少恢复参数的变异性，能更好地恢复前高斯分布的参数。在每个层面上，最大似然法和贝叶斯法之间总体差异不大。
