统计与数据科学系系列学术报告之四百三十五期

时    间：2024年6月7日（周五）16:00-17:00

地    点：李达三楼104室

主持人：复旦大学 管理学院 统计与数据科学系 戴国榕 博士

报告人：Prof. Yanyuan Ma  Pennsylvania State University

题   目：Prediction in Measurement Error Models

摘   要：We study the well-known difficult problem of prediction in measurement error models. By targeting directly at the prediction interval instead of the point prediction, we construct a prediction interval by providing estimators of both the center and the length of the interval which achieves a pre-determined prediction level. The constructing procedure requires a working model for the distribution of the variable prone to error. If the working model is correct, the prediction interval estimator obtains the smallest variability in terms of assessing the true center and length. If the working model is incorrect, the prediction interval estimation is still consistent. We further study how the length of the prediction interval depends on the choice of the true prediction interval center and provide guidance on obtaining minimal prediction interval length. Numerical experiments are conducted to illustrate the performance and a real data example is analyzed.

个人简介：Yanyuan Ma is Professor of Statistics at the Department of Statistics at the Pennsylvania State University. After graduating in mathematics from the Beijing University in 1994, she studied at Standard University for one year as a PhD student in Mathematics and then received her doctoral degree from the Massachusetts Institute of Technology in 1999. Before joining the Pennsylvania State University in 2016, she held faculty appointments at Texas A&M university, the University of Neuchatel and the University of South Carolina. Her main research activities are on semiparametrics and learning, including developing semiparametric methods and efficiency theory for measurement error models, dimension reduction, survival problems, case-control data and other data with selection bias. On these topics, she has co-authored a number of papers published in international journals and her research activities are supported by various funding agencies.

统计与数据科学系

2024-5-23