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Linear Mixed Effects Model with Functional Covariates
澳门新葡8455最新网站:2016年03月18日 00:00 点击数:

报告人:安百国

报告地点:澳门新葡8455最新网站105室

报告澳门新葡8455最新网站:2016年03月18日星期五16:00-17:00

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报告摘要:

The aim of this paper is to develop a class of linear mixed effects models with functional covariates (LMMFC). This development is motivated by the use of neuroimaging marker(s) to predict longitudinal clinical outcomes. A total variation penalty is introduced to characterize slope image(s) associated with the functional covariates in LMMFC. An Alternating Direction Method of Multipliers (ADMM) algorithm is proposed to simultaneously estimate the slope image and other unknown parameters, such as variance components in LMMFC. Theoretically, we establish an upper bound for the estimation error and prove the consistency of the variance component estimates. We also develop a bootstrap test procedure to carry out a global significance test for the slope image. Our simulations demonstrate a superior performance of LMMFC over many existing approaches. We use LMMFC to predict clinical scores by using a hippocampus covariate obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) study.

主讲人概况:

安百国,首都经济贸易大学统计新葡京最新官网讲师,2012年博士毕业于澳门新葡8455最新网站,师从郭建华教授;2013-2015年先后在北卡罗来纳大学统计与运筹系、生物统计系进行博士后访问,2012-至今在首都经济贸易大学统计新葡京最新官网做博士后工作,合作导师是纪宏教授。主要的研究兴趣包括回归压缩与选择、机器学习、超高维数据分析、文本挖掘和神经影像学分析等。

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