Eigenstructure inference for high-dimensional covariance with generalized shrinkage inverse-Wishart prior
高维协方差特征结构的广义收缩逆Wishart先验推断
In multivariate statistics, estimating the covariance matrix is essential for understanding the interdependence among variables. In high-dimensional settings, where the number of covariates increases with the sample size, it is well known that the eigenstructure of the sample covariance matrix is inconsistent. The inverse-Wishart prior, a standard choice for covariance estimation in Bayesian inference, also suffers from posterior inconsistency. To address the issue of…