【主题】L1-penalized Pairwise Difference Estimation for a High-dimensional Censored Regression Model
【报告人】潘哲文（副研究员，中山大学岭南学院）
【时间】2022年3月1日 星期二 09:30-11:00
【地点】Zoom链接：https://us02web.zoom.us/j/88593206613，会议ID：885 932 06613，会议密码：580002
【语言】中文
【摘要】High-dimensional data are nowadays readily available and increasingly common in various fields of empirical economics. This paper considers model selection and estimation for a high-dimensional censored linear regression model. We combine L1-penalization method with the ideas of Honoré and Powell (1994) and propose an L1-penalized pairwise difference least absolute deviations (LAD) estimator. Model selection consistency, estimation consistency and asymptotic normality of the estimator are established under regularity conditions. We also propose a post-penalized estimator that applies unpenalized pairwise difference LAD estimation to the model selected by the l1-penalized estimator, and find that the post-penalized estimator generally can perform better than the l1-penalized estimator in terms of the rate of convergence. Novel fast algorithms for computing the proposed estimators are provided based on the alternating direction method of multipliers. A simulation study is conducted to show the great improvements of our algorithms in terms of computation time and to illustrate the satisfactory statistical performance of our estimators.
【报告人简介】潘哲文，博士毕业于中山大学岭南学院，现为中山大学岭南学院副研究员。研究兴趣为微观计量经济学理论与应用，研究成果发表于Journal of Business & Economic Statistics、《中国科学：数学》等期刊，主持国家自然科学基金青年项目和面上项目各一项。
个人主页：https://lingnan.sysu.edu.cn/faculty/PanZhewen