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.

翻译：暂无翻译