An Empirical Bares Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices

• 成果类型: Article
• 署名作者: Yang, Chun-Hao; Doss, Hani; Vemuri, Baba C.
• 署名单位: National Taiwan University; State University System of Florida; University of Florida; State University System of Florida; University of Florida
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2110877
• 发表日期: 2024
• 页码: 259-272
• 关键词: exponential-families; statistics; covariance; distributions; eigenvalues

摘要

The James-Stein estimator is an estimator of the multivariate normal mean and dominates likelihood estimator (MLE) under squared error loss. The original work inspired great interest in developing shrinkage estimators for a variety of problems. Nonetheless, research on shrinkage estimation for manifold- valued data is scarce. In this article, we propose shrinkage estimators for the parameters of the Log-Normal distribution defined on the manifold of N x N symmetric positive-definite matrices. For this manifold, we choose the Log-Euclidean metric as its Riemannian metric since it is easy to compute and has been widely used in a variety of applications. By using the Log-Euclidean distance in the loss function, we derive a shrinkage estimator in an analytic form and show that it is asymptotically optimal within a large class of estimators that includes the MLE, which is the sample Frechet mean of the data. We demonstrate the performance of the proposed shrinkage estimator via several simulated data experiments. Additionally, we apply the shrinkage estimator to perform statistical inference in both diffusion and functional magnetic resonance imaging problems. Supplementary materials for this article are available online.