刘爱义教授讲座预告

发布日期:2019-06-11   字体:

讲座题目:Nonparametric Estimation of Distributions and Diagnostic Accuracy Based on Group-Tested Results with Differential Misclassification

主讲人:刘爱义教授

讲座时间:2019年6月13日(周四)下午3:00-4:00

讲座地点:综合楼601

主讲人简介:Prof. Aiyi Liu is a senior investigator and current Acting Chief in the Biostatistics and Bioinformatics Branch of the National Institute of Health and Human Development, USA. His research interests in statistical methods development center around sequential methodology and adaptive designs; robust methods for multidimensional outcomes, design, analysis; and methods for semicontinuous outcomes. Dr. Liu is an active member of several professional societies, including the American Statistical Association (ASA) and the International Chinese Statistical Association (ICSA). He is an elected fellow of ASA and is currently the president of ICSA. He is an adjunct professor in department of biostatistics, bioinformatics and biomathematics in Georgetown University. He has published over 160 papers in statistical research and is the recipient of a number of NIH and NICHD merit awards.

讲座摘要:This article concerns the problem of estimating a continuous distribution in a diseased or nondiseased population when only group-based test results on the disease status are available. The problem is challenging in that individual disease statuses are not observed and testing results are often subject to misclassification, with further complication that the misclassification may be differential as the group size and the number of the diseased individuals in the group vary. We propose a method to construct nonparametric estimation of the distribution and obtain its asymptotic properties. The performance of the distribution estimator is evaluated under various design considerations concerning group sizes and classification errors. The method is exemplified with data from the National Health and Nutrition Examination Survey (NHANES) study to estimate the distribution and diagnostic accuracy of blood monocyte percent in serum samples in predicting chlamydia incidence.


 


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