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Functional Additive Hazards Model with Application to COVID-19 Data-郝美玲

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  Abstract: We propose a new functional additive hazards model to investigate potential  effects of functional and scalar predictors on mortality risks, and develop a penalized least squares estimation approach for model parameters. The consistency, the convergence rate and the asymptotic distribution of the resulting estimator are established. In particular, deriving the asymptotic joint distribution of the infinite-dimensional and the finite-dimensional estimators poses great challenges. To tackle this problem, we design a framework of the Sobolev space equipped with a proper inner product and obtain a joint Bahadur representation of the estimators of functional and scalar parameters in the space. Using this key result, we further establish the asymptotic joint normality of the proposed estimators. Our simulation studies demonstrate that the proposed estimation procedure performs well. For illustration, we apply the proposed method to the COVID-19 data that motivated this research. The analysis results provide evidence to support the claim that minimizing community interactions indeed reduces mortality risks induced by COVID-19.

  简介: 郝美玲,对外经济贸易大学副教授,香港理工大学博士,多伦多大学附属医院玛格丽特公主癌症研究中心博士后。主要研究领域: 高维数据分析,生物统计,非参数统计,生物信息工程。

  

电竞投注  时间: 2020年10月8日(周四)19:30-20:30

电竞投注  地点: 线上腾讯会议(会议号: 666 7600 5472)

  联系人: 周洁

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