报告时间：6月30日上午10:00
报告地点：统计楼103
1.报告人：邹长亮，南开大学，教授，长江青年学者
Title: Multiple change-points detection in high dimension
Abstract: Change-point detection is an integral component of statistical modeling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are typically inapplicable. We propose a novel testing statistic by combining a modified Euclidean distance and an extreme statistic, and its null distribution is asymptotically normal. The new method naturally strikes a balance between the detection abilities for both dense and sparse changes, which gives itself an edge to potentially outperform existing methods. Furthermore, the number of change-points is determined by a new Schwarz’s information criterion together with a pre-screening procedure, and the locations of the change-points can be estimated via the dynamic programming algorithm in conjunction with the intrinsic order structure of the objective function. Under some mild conditions, we show that the new method provides consistent estimation with an almost optimal rate. Simulation studies show that the proposed method has satisfactory performance of identifying multiple change-points in terms of power and estimation accuracy.
2.报告人：王凯波，清华大学，教授
题目：卓越质量管理中的大数据分析
摘要：在质量管理中，基于大数据的分析与应用日趋重要。本报告关注工业大数据的分析及其在质量管理中的应用。报告基于三个层次的大数据分析思路，介绍不同的工业应用案例，试图寻找与质量管理与控制相关的研究机会。通过对单点数据、个体识别标签数据，以及群体识别标签数据的案例分析，揭示基于大数据统计分析带来的可能的研究机会与收获。
3.报告人：李健，西安交通大学，副教授
Title: Directional Change-Point Detection for Multivariate Categorical Data
Abstract: Most modern processes involve multiple quality characteristics that are all measured on attribute levels, and their overall quality is determined by these characteristics simultaneously. The characteristic factors usually correlate with each other, making multivariate categorical control techniques a must. We study Phase I analysis of multivariate categorical processes (MCPs) to identify the presence of change-points in the reference dataset. A directional change-point detection method based on log-linear models is proposed. The method exploits directional shift information and integrates MCPs into the unified framework of multivariate binomial and multivariate multinomial distributions. A diagnostic scheme for identifying the change-point location and the shift direction is also suggested. Numerical simulations are conducted to demonstrate the detection effectiveness and the diagnostic accuracy.