Sequential group Morris method with error control for efficient factor screening in simulation experiments
提出一种多阶段序贯分组Morris方法，利用因子方向性信息进行分组筛选，通过序贯概率比检验控制两类错误，在模拟实验中最高节省87.87%计算量且保持高精度。
The Morris elementary effects method (MM) is a widely used, model-free approach for factor screening and sensitivity analysis across various domains. However, traditional MM can be computationally demanding due to its “one-factor-at-a-time” nature. This paper presents a novel multi-stage sequential-group Morris method (SGMM) and a corresponding sequential implementation procedure tailored for both deterministic and stochastic simulation settings. SGMM leverages prior knowledge of the directional influence of individual factors to define elementary effects at the group level. This enables early elimination of factor groups with negligible group effects. Each stage employs a distribution-free sequential probability ratio test (SPRT) to evaluate the significance of group effects, ensuring rigorous control over Type I and Type II familywise error rates. Numerical experiments show that SGMM consistently outperforms existing simulation-based factor screening methods, delivering up to 87.87% computational savings while preserving high statistical accuracy.