A Stability Framework for Parameter Selection in the Minimum Covariance Determinant Problem
针对最小协方差行列式方法，提出基于稳定性的模型选择框架，通过自助法估计子集算法的不稳定性，帮助选择合适子集大小，适用于稳健估计和异常值检测。
The Minimum Covariance Determinant (MCD) method is a widely adopted tool for robust estimation and outlier detection. In this article, we introduce MCD model selection based on the notion of stability. Our best subset method leverages prior best practices such as statistical depths for initialization and concentration steps for subset refinement. Our contribution lies in constructing a bootstrap procedure to estimate the instability of the best subset algorithm. The instability path offers insights into a dataset’s inlier/outlier structure and facilitates suitable choice of the subset size. We rigorously benchmark the proposed framework against existing MCD variants and illustrate its practical utility on several real-world datasets.