Estimating the demand for health care with panel data: a semiparametric Bayesian approach
提出一种半参数贝叶斯随机效应模型，用狄利克雷过程先验灵活刻画医疗需求中的个体异质性和计数数据分布，并用德国数据验证。
This paper is concerned with the problem of estimating the demand for health care with panel data. A random effects model is specified within a semiparametric Bayesian approach using a Dirichlet process prior. This results in a very flexible distribution for both the random effects and the count variable. In particular, the model can be seen as a mixture distribution with a random number of components, and is therefore a natural extension of prevailing latent class models. A full Bayesian analysis using Markov chain Monte Carlo simulation methods is proposed. The methodology is illustrated with an application using data from Germany.