5 papers
Directional Kernel Mean Difference: A Fast Signed Statistic for Univariate Distribution Comparison
Shijie Zhong, Jiangfeng Fu
We introduce the Directional Kernel Mean Difference (DKMD), a signed statistic for univariate distribution comparison that preserves the direction of distributional shifts. Unlike…
Analytical Extraction of Conditional Aleatory Sensitivities Across Epistemic Space via a Single PCE Model
Shijie Zhong, Huiyou Tan, Jiangfeng Fu
In hybrid uncertainty quantification, evaluating how aleatory sensitivities vary under epistemic uncertainty, referred to as conditional Sobol' indices, is typically hindered by th…
Hybrid Uncertainty Sensitivity Analysis Based on the HSIC for High-Dimensional Responses with Aleatory--Epistemic Separation
Shijie Zhong, Jiangfeng Fu, Pengfei Wei
Quantifying the influence of hybrid aleatory and epistemic uncertainties on high-dimensional system responses remains a major challenge in global sensitivity analysis (GSA). Existi…
Analytical Extraction of Conditional Sobol' Indices via Basis Decomposition of Polynomial Chaos Expansions
Shijie Zhong, Jiangfeng Fu
In uncertainty quantification, evaluating sensitivity measures under specific conditions (i.e., conditional Sobol' indices) is essential for systems with parameterized responses, s…
Unified Unbiased Variance Estimation for Maximum Mean Discrepancy: Robust Finite-Sample Performance with Imbalanced Data and Exact Acceleration under Null and Alternative Hypotheses
Shijie Zhong, Yikun Yang, Da Gong +1
The maximum mean discrepancy (MMD) is a kernel-based nonparametric statistic for two-sample testing, whose inferential accuracy depends critically on variance characterization. Exi…