報告題目:Regression clustering and its application
報 告 人:吳月華（加拿大約克大學教授，國際統計學會會員）
報告新澳博官网: 2019年2月25日(周一) 16:30-17:30
報告摘要：Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point and estimating the regression coefficients of the model. In this talk, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. We also present simulation studies for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. We end the talk by briefly discussing orthogonal regression clustering. Joint work with G. Qian and others.