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学院系列学术报告(五)
发布日期:2018-06-26 浏览次数:

报告题目Prediction Accuracy Measures for a Nonlinear Model and for Right-Censored Time-to-Event Data

报告人李刚

报告摘要In this talk I will discuss a pair of new prediction summary measures for a nonlinear prediction function with right-censored time-to-event data.  The first measure, defined as the proportion of explained variance by a linearly corrected prediction function, quantifies the potential predictive power of the nonlinear prediction function. The second measure, defined as the proportion of explained prediction error by its corrected prediction function, gauges the closeness of the prediction function to its corrected version and serves as a supplementary measure to indicate (by a value less than 1) whether the correction is needed to fulfill its potential predictive power and quantify how much prediction error reduction can be realized with the correction. The two measures together provide a complete summary of the predictive accuracy of the nonlinear prediction function.  We motivate these measures by first establishing a variance decomposition and a prediction error decomposition at the population level and then deriving uncensored and censored sample versions of these decompositions. We note that for the least square prediction function under the linear model with no censoring, the first measure reduces to the classical coefficient of determination and the second measure degenerates to 1.  We show that the sample measures are consistent estimators of their population counterparts and conduct extensive simulations to investigate their finite sample properties. A real data illustration will be given.

报告时间7月1日上午11:30-12:00

报告地点:科技楼二楼北会议室

报告人简介:李刚,加州大学洛杉矶分校生物与生物数学系教授。美国学会Fellow,国际数理学会Fellow,英国皇家学会Fellow。主要研究兴趣为生存分析,纵向数据分析,高维数据分析,临床试验数据分析等。个人主页:http://faculty.biostat.ucla.edu/gangli


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