Multithreshold change plane model: Estimation theory and applications in subgroup identification
报告人： 栗家量 (新加坡国立大学）
We propose a multithreshold change plane regression model which naturally partitions the observed subjects into subgroups with different covariate effects. The underlying grouping variable is a linear function of observed covariates and thus multiple thresholds produce change planes in the covariate space. We contribute a novel two‐stage estimation approach to determine the number of subgroups, the location of thresholds, and all other regression parameters. In the first stage we adopt a group selection principle to consistently identify the number of subgroups, while in the second stage change point locations and model parameter estimates are refined by a penalized induced smoothing technique. Our procedure allows sparse solutions for relatively moderate‐ or high‐dimensional covariates. We further establish the asymptotic properties of our proposed estimators under appropriate technical conditions. We evaluate the performance of the proposed methods by simulation studies and provide illustrations using two medical data examples. Our proposal for subgroup identification may lead to an immediate application in personalized medicine.
About the Speaker:
栗家量于中国科学技术大学统计系取得本科学士，后于美国威斯康星大学取得统计学博士学位。现任职于新加坡国立大学统计与数据科学系教授。最近研究方向包括变点，personalized medicine, diagnostic medicine, survival analysis, structural equation model, nonparametric model.发表科研论文200多篇。他是ASA(美国统计学会)和IMS（数理统计学会）的选举会士Fellow.曾担任biometrics和lifetime data analysis的AE.
Your participation is warmly welcomed!