时 间：2023年3月28日10:00-11:00
地 点：腾讯会议ID：942-760-691 密码：2023
报告人：余涛 新加坡国立大学 教授
主持人：刘玉坤 华东师范大学 教授
摘 要：
The Box-Cox transformation model has been widely applied for many years. The parametric version of this model assumes that the random error follows a parametric distribution, say the normal distribution, and estimates the model parameters using the maximum likelihood method. The semiparametric version assumes that the distribution of the random error is completely unknown; existing methods either need strong assumptions, or are less effective when the distribution of the random error significantly deviates from the normal distribution. We adopt the semiparametric assumption and propose a maximum profile binomial likelihood method. We theoretically establish the joint distribution of the estimators of the model parameters. Through extensive numerical studies, we demonstrate that our method has an advantage over existing methods when the distribution of the random error deviates from the normal distribution. Furthermore, we compare the performance of our method and existing methods on an HIV data set.
报告人简介：
Dr. YU, Tao received his B.S. degree and M.S. in Mathematics and Probability & Statistics from Nankai University in 2001 and 2004 respectively. He obtained his Ph.D. degree from University of Wisconsin-Madison in 2009. He was assistant professor from September 2009 to December 2016 in Department of Statistics and Data Science (DSDS) at National University of Singapore (NUS), and now he is associate professor in DSDS at NUS.