主题：A general framework to extend sufficient dimension reductions to the cases of the mixture multivariate elliptical distributions

时间：4月2日12:30

地点：腾讯会议376419906

主持人：罗琳教授

报告人简介：

陈飞，教授，统计学博士，博士生导师，云南财经大学统计与数学学院院长。云南省中青年学术和技术带头人后备人才，中国现场统计研究会旅游大数据学会副理事长，云南省应用统计学会常务理事。主要从事降维理论、统计诊断、结构方程模型、半参数贝叶斯理论等领域的研究。主持国家自然科学基金项目4项，其研究成果获云南省哲学社会科学优秀成果奖。

讲座简介：

In the sufficient dimension reduction (SDR), many methods depend on some assumptions on the distribution of predictor vector, such as the linear design condition (L.D.C.), the assumption of constant conditional variance, and so on. The mixture distributions emerge frequently in practice, but they may not satisfy the above assumptions. In this article, a general framework is proposed to extend various SDR methods to the cases where the predictor vector follows the mixture elliptical distributions, together with the asymptotic property for the consistency of the kernel matrix estimators. For illustration, the extensions of several classical SDR approaches under the proposed framework are detailed. Moreover, a method to estimate the structural dimension is given, together with a procedure to check an assumption called homogeneity. The proposed methodology is illustrated by simulated and real examples.