题目： BOLT-SSI: Fully Screening Interaction Effects for Ultra-High Dimensional Data

报告人：彭衡 博士 （香港浸会大学）

主持人：晏艳阳教授

时间：2018年11月01日（星期四）上午10：00—11：00

地点：9778818威尼斯财院校区行政楼315

Detecting interaction effects among predictor variables and response variables is often a crucial step in regression modelling of real data for various applications. In this paper, we firstly introduce a simple sure screening procedure (SSI) for marginal likelihood to fully detect significant pure interactions between predictor variables and the response variable in the high or ultra-high dimensional generalized linear regression models. The major challenge of SSI to detect interactions comes from computational efficiency. To make SSI applicable to detect interactions in ultra-high dimensional setting, we boost the conventional SSI by discretizing continuous predictor variables, where the Boolean operation is applied in the presence of the marginal likelihood estimates, and call it BOLT-SSI. Next, we show statistical properties of SSI and BOLT-SSI, i.e., they have the sure screening property for even exponentially growing dimensionality and can control the false discovery rate at a reasonable size. To demonstrate the advantages of BOLT-SSI over existing methods, we conducted extensive simulation studies and applied BOLT-SSI to three real data sets such as Residential Building data, Supermarket data and Northern Finland Birth Cohort data. The results support our theoretical perspectives, and show that BOLT-SSI can boost the computational efficiency of sure screening for interaction effects in the presence of high-dimensional predictor variables and simultaneously improve the prediction performance in real data analysis.

报告人简介：

彭衡博士，先后毕业于中国科技大学，香港中文大学，师从世界著名统计学家范剑青教授，现任香港浸会大学数学系副教授（终身职位），香港浸会大学深圳研究院研究员，国际统计研究院当选会员，数理统计研究所终生会员。主要从事非参数与半参数模型、模型选择、高维数据建模、混合模型，金融计量经济学等领域的研究，在Annals of Statistics、JASA、Biometrika等国际著名统计学杂志发表40余篇高质量论文，google学术引用1500余次。担任国际期刊Statistica Sinica、Computational Statistics & Data Analysis的副主编，主持完成多项美国自然科学基金、香港研资局、国家自然科学基金。