Parsimonious Estimation of the Covariance Matrix in Multinomial Probit Models
提出一种贝叶斯方法，对多项Probit模型中潜变量协方差矩阵的逆矩阵元素施加零约束，实现协方差矩阵的简约表示，并通过模拟和实际数据验证了该方法能更高效地估计协方差矩阵和回归系数。
This article presents a Bayesian analysis of a multinomial probit model by building on previous work that specified priors on identified parameters. The main contribution of our article is to propose a prior on the covariance matrix of the latent utilities that permits elements of the inverse of the covariance matrix to be identically zero. This allows a parsimonious representation of the covariance matrix when such parsimony exists. The methodology is applied to both simulated and real data, and its ability to obtain more efficient estimators of the covariance matrix and regression coefficients is assessed using simulated data.