Change Line Classification and Regression

• Activity Number: 357
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Chaeryon Kang*+ and Fei Zou and Hao Zhu and Michael Kosorok
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
• Keywords: Change-line ; Latent Subgroups ; Change-point ; Chemical Toxicity activity

Abstract

We introduce the "change-line" classification and regression method to study latent subgroups. The proposed method finds a line which optimally divides a feature space into two heterogeneous subgroups, each of which yields a response having a different probability distribution or having a different regression model. The procedure is useful for classifying biochemicals on the basis of toxicity, where the feature space consists of chemical descriptors and the response is toxicity activity. The split-line algorithm is utilized to reduce computational complexity. A two step estimation procedure is described. Two sets of simulation studies and a data analysis applying our method to rat acute toxicity data are presented to demonstrate utility of the proposed method. A graphical examination is performed to verify the existence of an underlying change in the distribution of toxicity activity.