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20171110 李启寨:Order-Restricted Inference for Correlated and Clustered ROC Data
时间:2017-11-10

题目:Uniform Projection Designs

主讲:李启寨

时间:2017年11月10日  14:30-15:30

地点:明德主楼 1030

摘要:

Estimating the receiver operating characteristic (ROC) curve, which is commonly used to evaluate and compare the accuracy of diagnostic models and biometric systems, has been an important problem in diagnostic medicine, biometric recognition, signal detection, and others. In a variety of applications, the data are generally collected under two or more ordered experimental conditions, which accordingly results in a natural stochastic ordering among the observations under these different experimental conditions. More importantly, statistical inference incorporating such a stochastic ordering condition is expected to improve estimation efficiency. Clustered and correlated data occur when multiple measurements are gleaned from the same subject. In such situation, the estimation of ROC curves becomes more complicated due to unknown within-subject correlations. Although methods are available for the estimation of ROC curves from clustered data, to the best of our knowledge, how to impose natural ordering on the estimation of ROC curves has not been studied yet. In this article, we propose an ordered-restricted estimator for the ROC curve, as well as the area under the curve and the partial area under the curve to accommodate the clustered and correlated data structure. We derive asymptotic properties of the proposed order-restricted estimators and theoretically show that they possess lower mean-squared errors than the existing estimators. Simulation studies demonstrate better performance of the newly proposed estimators over existing methods for finite samples. The proposed method is further illustrated using the fingerprint matching data from the National Institute of Standards and Technology Special Database 4.

简介:

李启寨,中国科学院数学与系统科学研究院研究员,2001年本科毕业于中国科学技术大学,2006年博士毕业于中国科学院数学与系统科学研究院。主要研究方向为生物医学统计。在顶尖期刊 Nature Genetics, JASA 已发表学术论文80余篇。曾获优秀青年科学基金、国际统计学会推选会员,中国工业与应用数学学会优秀青年学者奖等。