报告题目：Simultaneous Outlier Detection and Prediction for Kriging with True Identification
报告人：王占锋 副教授
报告时间：2026年5月27日（周三）下午 15:50-16:50
报告地点：藕舫楼629室
主持人：曹春正 教授
报告人简介：
王占锋，博士生导师，分别于2003年和2008年获中国科学技术大学学士和理学博士学位。主要从事生物统计、函数型数据分析、非欧数据分析等领域的研究，在国内外学术期刊：Journal of the American Statistical Association、Journal of Business & Economic Statistics、Journal of Computational and Graphical Statistics、Biometrics、中国科学英文版等上发表论文60多篇。曾主持国家自然科学青年基金一项和面上基金两项，参与国家重点自然科学基金两项。中国现场统计研究会旅游大数据分会理事长，全国工业统计学教学研究会地学数据分会副理事长。
报告简介：
Kriging with interpolation is widely used in various noise-free areas, such as computer experiments. However, owing to its Gaussian assumption, it is susceptible to outliers, which affects statistical inference, and the resulting conclusions could be misleading. Little work has explored outlier detection for kriging. Therefore, we propose a novel kriging method for simultaneous outlier detection and prediction by introducing a normal-gamma prior, which results in an unbounded penalty on the biases to distinguish outliers from normal data points. We develop a simple and efficient method, avoiding the expensive computation of the Markov chain Monte Carlo algorithm, to simultaneously detect outliers and make a prediction. We establish the true identification property for outlier detection and the consistency of the estimated hyperparameters in kriging under the increasing domain framework as if the number and locations of the outliers were known in advance. Under appropriate regularity conditions, we demonstrate information consistency for prediction in the presence of outliers. Numerical studies and real data examples show that the proposed method generally provides robust analyses in the presence of outliers.
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数学与统计学院
江苏省应用数学（南京信息工程大学）中心
江苏省系统建模与数据分析国际合作联合实验室
江苏省统计科学研究基地
2026年5月25日
