A correlation-shrinkage prior for Bayesian prediction of the two-dimensional Wishart model
研究二维Wishart模型的贝叶斯预测问题，构造了一个尺度不变且排列不变的先验分布来收缩相关系数，并证明基于该先验的预测密度是极小化的。
Summary A Bayesian prediction problem for the two-dimensional Wishart model is investigated within the framework of decision theory. The loss function is the Kullback–Leibler divergence. We construct a scale-invariant and permutation-invariant prior distribution that shrinks the correlation coefficient. The prior is the geometric mean of the right invariant prior with respect to permutation of the indices, and is characterized by a uniform distribution for Fisher’s $z$-transformation of the correlation coefficient. The Bayesian predictive density based on the prior is shown to be minimax.