報告題目：Additive hazards regression for misclassified current status data
報 告 人：李樹威 副教授 廣州大學
報告時間：2021年3月22日 14:30-15:30
報告地點：騰訊會議ID：576 793 038
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校内聯系人：程建華 chengjh@jlu.edu.cn
報告摘要：
We discuss regression analysis of current status data with the additive hazards model when the failure status may suffer misclassification. Such data occur commonly in many scientific fields involving the diagnosis test with imperfect sensitivity and specificity. In particular, we consider the situation where the sensitivity and specificity are known and propose a nonparametric maximum likelihood approach. For the implementation of the method, a novel EM algorithm is developed, and the asymptotic properties of the resulting estimators are established. Furthermore, the estimated regression parameters are shown to be semiparametrically efficient. We demonstrate the empirical performance of the proposed methodology in a simulation study and show its substantial advantages over the naive method. Also an application to a motivated study on chlamydia is provided.
報告人簡介：
李樹威，廣州大學統計系副教授，研究生導師，研究領域為生物統計、生存分析、縱向數據等。目前已發表10餘篇高水平科研論文，主持國家自然科學基金1項。
