时 间：2026年11月6日（周五）11：00 - 12：00
地 点：普陀校区理科大楼A614室
报告人：李文龙 北京交通大学副教授
邀请人：王亚平 华东师范大学统计学院
摘 要：
Latin hypercube designs are widely used for running computer experiments. Motivated by modern computer experiments with grouped inputs, we propose a new type of Latin hypercube design, called grouped Latin hypercube design with controlled correlations (GLHD-CC). This special Latin hypercube partitions all columns into disjoint groups where controlled column-wise correlations across all inputs is achieved and inputs within the same group have lower column-wise correlations than those from different groups. Our construction method for this new design uses a multivariate block orthogonalization technique in multivariate linear regression to simultaneously control correlations across multiple variables. We present an efficient algorithm for enabling the construction method and establish its theoretical properties. The algorithm is flexible in run size and input dimension, with effectiveness demonstrated through three popular test functions and a case study on a true black-box simulator.
报告人简介：
李文龙，北京交通大学数学与统计学院副教授、博士生导师。南开大学统计学博士，博士期间赴加拿大西蒙弗雷泽大学联合培养一年，后在北京理工大学从事博士后研究。主要从事试验设计与计算机试验研究，相关成果发表于 Biometrika、Technometrics、Journal of Quality Technology、SIAM/ASA Journal on Uncertainty Quantification、《中国科学》等国内外重要期刊。