时 间：2023年6月28日 14:00-15:00
地 点：理科大楼A1714
报告人：王春燕中国人民大学助理教授
主持人：王亚平华东师范大学研究员
摘 要：
The space-filling property and orthogonality are perhaps two most desirable design properties for computer experiments. The space-filling property is appropriate for Gaussian process models, while orthogonality allows the estimated effects to be uncorrelated. This paper presents a general approach for constructing a rich class of orthogonal designs with attractive space-filling properties. This is apparently new in the literature. The construction methods are straightforward to implement. Their theoretical supports are established. Moreover, the resulting designs are flexible in the run sizes.
报告人简介：
Dr. Chunyan Wang graduated from Nankai University with a degree in Statistics, and her research interests include experimental designs, computer experiments, and order-of-addition experiments, etc. Her supervisor is Prof. Minqian Liu. During her Ph.D., Chunyan Wang visited the University of Tennessee, USA, and worked under the supervision of Professor Robert Mee. After completing her PhD, Chunyan Wang joined the Department of Statistics at Purdue University as a postdoctoral research assistant in July 2021, and continued her research work in statistical experimental design under the supervision of Prof. Dennis Lin. Then she joined the School of Statistics of Renmin University of China as a lecturer in August 2022. Currently, Chunyan Wang has 6 papers accepted and published online in Annals of Statistics, Statistica Sinica, Journal of Quality Technology and Journal of Statistical Planning and Inference.