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      當前位置: 華中科技大學數學與統計學院 > 科學研究 > 學術活動 > 正文



      2019-12-26 11:53:30    瀏覽次數:

      字體 色彩方案

      報告人:Song Xinyuan 宋心遠 教授(香港中文大學)

      報告題目:Joint Modeling of Longitudinal Imaging and Survival Data

      報告摘要:This study considers a joint modeling framework for simultaneously examining the dynamic pattern of longitudinal and ultrahigh-dimensional images and their effects on the survival of interest. A functional mixed effects model is considered to describe the trajectories of longitudinal images.  Then, a high-dimensional functional principal component analysis (HD-FPCA) is adopted to extract the principal eigenimages to reduce the ultrahigh dimensionality of the imaging data. Finally, a Cox regression model is used to examine the effects of the longitudinal images and other covariates on the hazards of interest.  A theoretical justification shows that a naive two-stage procedure that separately analyzes each part of the joint model produces biased estimation. We develop a Bayesian joint estimation method coupled with efficient Markov chain Monte Carlo sampling schemes to perform statistical inference for the proposed joint model. Moreover, a Monte Carlo dynamic prediction procedure is proposed to predict the survival probabilities of future subjects given their historical longitudinal images. The proposed method is assessed through simulation studies and applied to the study of Alzheimer's Disease Neuroimaging Initiative. New insights into the early diagnosis and prevention of Alzheimer's disease are obtained.

      報告人簡介:宋心遠,香港中文大學統計系主任。宋心遠教授的研究方向是潛變量模型,貝葉斯方法,統計計算和生存分析等众创彩票-首页众创彩票。同時還擔任多個國際期刊包括《Psychometrika》,《Biometrics》众创彩票-首页众创彩票,《Computational Statistics & Data Analysis》和《Structural Equation Modeling: A Multidisciplinary Journal》的副主編或編委。已在國際期刊發表超過100篇論文,近期論文主要發表于《Journal of the American Statistical Association》,《Biometrika》众创彩票-首页众创彩票,《Biometrics》,《Bioinformatics》众创彩票-首页众创彩票,《Psychometrika》,《Quantitative Finance》等期刊。

      報告時間:2019年12月26日(星期四)下午3:00 - 5:00