Bayesian estimation of a random effects heteroscedastic probit model
研究了允许异方差的随机效应二元Probit模型的贝叶斯分析，通过模拟和实例表明忽略异方差会导致估计偏误和预测不佳，并采用多种模型比较方法。
Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.