🤖 AI Summary
This work addresses the problem of multiple hypothesis testing for multivariate Gaussian means under arbitrary covariance dependence structures. The authors propose a novel approach that integrates maximum residual descent (MRD) with a multi-stage calibration scheme. By introducing a new representation of residual statistics based on a single active precision matrix, the method achieves covariance-adaptive residualization, substantially reducing computational complexity. It replaces model-dependent thresholds with a simple multi-stage calibration rule, combining generalized stepwise critical values and precision matrix reconstruction techniques. The proposed procedure significantly lowers the normalized misclassification risk across diverse dependence structures, achieving error discovery rate control close to the nominal level, extremely low missed detection rates, near-perfect statistical power, and accurate estimation of the number of true signals.
📝 Abstract
In this paper, we study simultaneous hypothesis testing for multivariate Gaussian means under arbitrary covariance dependence. Building on the Maximum Residual Down (MRD) procedure of Cohen et al. (2009), we investigate a new calibration strategy based on the generalized step-down critical constants of Gavrilov et al. (2009). The resulting procedure retains the covariance-adaptive residualization mechanism of MRD while replacing the original model-dependent threshold specification with a simple stagewise calibration rule. Since the proposed procedure belongs to the class of monotone residual-based step-down procedures studied by Ghosh and Chakrabarti (2026), its admissibility follows directly from their theory. We also derive alternative representations of the MRD residual statistics that express all active residuals through a single active precision matrix, substantially reducing computational complexity. Simulation studies across a broad range of dependence structures show that the proposed methodology often achieves a lower normalized misclassification risk than several widely used marginal testing procedures. Under several structured dependence models, the procedure also exhibits strong signal-recovery behavior, attaining false discovery rates near the nominal level, extremely small false non-discovery rates, powers approaching one, and average numbers of rejections close to the expected number of true signals. These findings provide empirical evidence that covariance-adaptive residualization and stagewise calibration may interact in a highly favorable manner for dependent multiple testing.