时间：2019年12月19日（周四）11:00-12:00
地点：中北校区理科大楼A1716报告厅
题目：Left without being seen: The disappearance of impatient patients, combining current-status, right-censored and left-censored data
报告人：Yair Goldberg Associate Professor Technion-Israel Institute of Technology
摘要：
I will present a survival-data setting that combines right-censored, left-censored, and current status data. The motivation is the challenge to estimate patience time, namely, the time till leaving without being served, of patients who arrive at an emergency department, and wait for treatment. Three categories of patients are observed. The first category consists of patients who get service, and thus their patience time is right-censored by the waiting time. The second category comprises those who leave the system and announce it, and therefore their patience time is observed while the waiting time is right-censored. The third category consists of patients who leave the system without announcing it; their absence is hence revealed only when they are called to service, which is after they have already left; formally, their patience time is left-censored. The goal is to estimate the (im)patience distribution, based on these three data categories. I will present a novel methodology for distribution estimation using both parametric and nonparametric techniques. I will also present the performance of these estimators via simulated data and data from emergency departments.
This is joint work with J. Yefenof from The Hebrew University, J. Wiler, from the University of Colorado, A. A. Mandelbaum from the Technion, and Y, Ritov from the University of Michigan.
报告人简介：
I obtained my Ph.D. in Statistics from the Hebrew University of Jerusalem in 2009. From 2009 to 2011, I conducted postdoctoral studies at the Department of Biostatistics at the University of North Carolina at Chapel Hill. From 2011 to 2018, I worked and taught at the Department of Statistics at the University of Haifa. Since 2018, I am a faculty member at the Faculty of Industrial Engineering and Management in the Technion.
My research interests include statistical theory and machine learning. I currently work on research topics in both of these fields, and at the interface between them. In the field of statistical learning, I work on the development and analysis of new models both in the semiparmetric and nonparametric framework. In the field of machine learning, I currently work on developing fast algorithms that can handle missing and survival data. In the interface between the fields of statistical theory and machine learning, I work on the development of tools to measure the uncertainty of estimators obtained by machine learning techniques such as kernel machines and deep learning. In addition, I work on several applied projects as a biostatistician.