A finite mixture analysis of beauty-contest data using generalized beta distributions
提出一种基于贝塔分布的混合模型，用于分析多组实验的美人竞赛数据，发现该模型比正态混合模型能更好拟合数据，并支持多数个体遵循迭代最优反应推理模式的假设。
Abstract This paper introduces a mixture model based on the beta distribution, without pre-established means and variances, to analyze a large set of Beauty-Contest data obtained from diverse groups of experiments (Bosch-Domènech et al. 2002). This model gives a better fit of the experimental data, and more precision to the hypothesis that a large proportion of individuals follow a common pattern of reasoning, described as Iterated Best Reply (degenerate), than mixture models based on the normal distribution. The analysis shows that the means of the distributions across the groups of experiments are pretty stable, while the proportions of choices at different levels of reasoning vary across groups.