| 主题： |
Sufficient Dimension Reduction for Multiple Populations |
| 类型： |
学术报告
|
| 主办方： |
|
| 报告人： |
文学荣（Missouri University） |
| 日期： |
5月24日下午3点-4点 |
| 地点： |
知新楼B-1248 |
| 内容： |
Title: Sufficient Dimension Reduction for Multiple Populations
Abstract: Two topics in the area of dimension reduction for multiple populations will be explored. We will first propose a link-free test for testing whether two (or more) multi-index models share identical indices via the sufficient dimension reduction approach. Test statistics are developed based upon three different sufficient dimension reduction methods: (i) sliced inverse regression, (ii) sliced average variance estimation and (iii) directional regression. The asymptotic null distributions of our teststatistics are derived. Next, we will discuss model-free shrinkage variable selection via sufficient dimension reduction for multiple data sets.
报告时间：5月24日下午3点-4点
地点：知新楼B-1248
Meggie Wen （文学荣）
Associate Professor in Statistics
Department of Mathematics and Statistics
Missouri University of Science and Technology |
