报告题目：Simultaneous Variable selcetion and Estimation for Joint Models of Longitudinal and Failure Time Data with Interval Censoring

报 告 人：易凤婷

研究方向：纵向数据与生存数据联合模型

报告摘要：This paper discusses variable selection in the context of joint analysis of lon- gitudinal data and failure time data. A large literature has been developed for either variable selection or the joint analysis but there exists only limited litera- ture for variable selection in the context of the joint analysis when failure time data are right censored. Corresponding to this, we will consider the situation where instead of right-censored data, one observes interval-censored failure time data, a more general and commonly occurring form of failure time data. For the problem, a class of penalized likelihood-based procedures will be developed for simultaneous variable selection and estimation of relevant covariate effects for both longitudinal and failure time variables of interest. In particular, a Monte Carlo EM (MCEM) algorithm is presented for the implementation of the pro- posed approach. The proposed method allows for the number of covariates to be diverging with the sample size and is shown to have the oracle property. An extensive simulation study is conducted to assess the finite sample performance of the proposed approach and indicates that it works well in practical situations. An application is also provided.

报告题目：Bayesian Tensor Logistic Regression with Applications to Neuroimaging Data Analysis of Alzheimer's Disease

报 告 人：吴莹

研究方向：医学成像数据的学习方法

摘 要：Alzheimer's disease (AD) can be diagnosed by utilizing traditional logistic regression models to fit magnetic resonance imaging (MRI) data of brain, which is regarded as a vector of covariates. But its parameter estimation is inefficient and computationally extensive due to ultrahigh dimensionality and complicated structure of MRI data. To overcome this deficiency, this paper proposes a tensor logistic regression model (TLRM) for AD's MRI data by regarding MRI tensor as covariates. Under this framework, a tensor (CP) decomposition tool is employed to reduce ultrahigh dimensional tensor to a high dimensional level, a novel Bayesian adaptive Lasso method is developed to simultaneously select important components of tensor and estimate model parameters by incorporating the Polya-Gamma method leading a closed-form likelihood and avoiding the usage of the Metropolis-Hastings algorithm, and Gibbs sampler technique in Markov chain Monte Carlo (MCMC). A tensor's product technique is utilized to optimize the calculation program and speed up the calculation of MCMC. Bayes factor together with the path sampling approach is presented to select tensor rank in CP decomposition. Effectiveness of the proposed method is illustrated on simulation studies and an MRI data analysis.

报告题目：银河宇宙线及其超新星遗迹起源

报 告 人：鲍必文

研究方向：高能天体物理

报告摘要：人类对宇宙线的研究已逾一个世纪，但其起源问题仍未得到解决。目前普遍认为，超新星遗迹是银河宇宙线的主要起源地，其前向激波可通过扩散激波加速机制加速粒子。超新星遗迹的演化涉及粒子加速与逃逸、抛射物质与星际介质的相互作用、多波段非热辐射、高温气体的过电离、金属元素增丰、星系结构的形成与演化等重要物理过程，是研究高能天体物理的理想探针。故此，开展超新星遗迹动力学演化及其性质的研究对深入理解极端条件下的物理过程、探索银河宇宙线的起源具有重要的科学意义。

时 间：2023年3月31日周五14：00——17：00

地 点：数学与统计学院格物楼3103报告厅

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