Bayesian inference in a sample selection model
开发了样本选择模型的贝叶斯推断方法，先给出误差正态分布下的吉布斯抽样算法，再扩展到非正态情形，用狄利克雷过程先验将不可观测分布建模为混合正态，能同时检测选择效应和偏离正态性，并用模拟数据和RAND健康保险实验数据演示。
This paper develops methods of Bayesian inference in a sample selection model. The main feature of this model is that the outcome variable is only partially observed. We first present a Gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed. The algorithm is then extended to analyze models that are characterized by nonnormality. Specifically, we use a Dirichlet process prior and model the distribution of the unobservables as a mixture of normal distributions with a random number of components. The posterior distribution in this model can simultaneously detect the presence of selection effects and departures from normality. Our methods are illustrated using some simulated data and an abstract from the RAND health insurance experiment.