時(shí)    間：2024年11月01日 10:00 - 11:00

報(bào)告人：栗家量新加坡國(guó)立大學(xué)教授

地   點(diǎn)：普陀校區(qū)理科大樓A1114

主持人：馬慧娟華東師范大學(xué)副教授

摘   要：

Model averaging is an attractive ensemble technique to construct fast and accurate prediction. Despite of having been widely practiced in cross-sectional data analysis, its application to longitudinal data is rather limited so far. We consider model averaging for longitudinal response when the number of covariates is ultrahigh. To this end, we propose a novel two-stage procedure in which variable screening is first conducted and then followed by model averaging. In both stages, a robust rank-based estimation function is introduced to cope with potential outliers and heavy-tailed error distributions, while the longitudinal correlation is modelled by a modified Cholesky decomposition method and properly incorporated to achieve efficiency. Asymptotic properties of our proposed methods are rigorously established, including screening consistency and convergence of the model averaging predictor, with uncertainties in the screening step and selected model set both taken into account. Extensive simulation studies demonstrate that our method outperforms existing competitors, resulting in significant improvements in screening and prediction performance. Finally, we apply our proposed framework to analyse a human microbiome dataset, showing the capability of our procedure in resolving robust prediction using massive metabolites.

報(bào)告人簡(jiǎn)介：

栗家量，新加坡國(guó)立大學(xué)統(tǒng)計(jì)與應(yīng)用概率系教授，同時(shí)在杜克大學(xué)-新加坡國(guó)大醫(yī)學(xué)院兼職教授。栗教授于2001年中國(guó)科學(xué)技術(shù)大學(xué)獲得統(tǒng)計(jì)學(xué)學(xué)士學(xué)位，分別于2005年和2006年在美國(guó)威斯康星大學(xué)麥迪遜分校獲得公共健康學(xué)碩士學(xué)位和統(tǒng)計(jì)學(xué)博士學(xué)位。現(xiàn)在研究興趣包括工具變量、子集分析、變點(diǎn)模型、結(jié)構(gòu)方程、精準(zhǔn)醫(yī)學(xué)、診斷醫(yī)學(xué)、模型平均、非參、生存分析等。已發(fā)表論文160余篇，他是ASA和IMS的Fellow和ISI的Elected Member。