A correlation-shrinkage prior for Bayesian prediction of the two-dimensional Wishart model
二维Wishart模型贝叶斯预测的相关系数收缩先验
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 $z$-transf…