Year | Month |
2024 | Jan Feb Mar May Jun Jul |
2023 | Jan Feb Mar Apr May Jun Jul Aug Oct Nov Dec |
2022 | Oct Nov Dec |
Title: | Learning Covariance Structures of Multidimensional Linear Mixed Models |
Speaker: | Professor Yuedong Wang, Department of Statistics and Applied Probability, University of California, Santa Barbara, USA |
Time/Place: | 14:00 - 15:00 FSC1217 |
Abstract: | We propose a computationally efficient framework for the estimation of multidimensional response mixed-effects models. The key idea is to formulate a moment error function to measure the error of covariance estimates. Minimizing this loss entails solving a nonlinear semidefinite programming problem, which can be solved efficiently by a proximal gradient algorithm. The proposed method is much faster than some of the existing likelihood-based methods while maintaining comparable accuracy across a range of simulations. We provide non-asymptotic analysis to study various concentration properties of the covariance estimators, which have not previously been investigated in the studies of mixed-effects models. |
Title: | Multi-state Model and Structural Selection for the Analysis of Depressive Symptom Dynamics in Middle-aged and Older Adults |
Speaker: | Dr. Chuoxin Ma, Department of Statistics and Data Science, BNU-HKBU United International College, Zhuhai, China |
Time/Place: | 15:00 - 16:00 FSC1217 |
Abstract: | Depressive symptoms are increasingly common in middle-aged and older adults. People may experience transitions across different underlying states due to symptom severity fluctuation over a course of many years. Characterising the dynamics of depression and identifying key factors associated with different depressive states is important for the development of effective interventions. We proposed a multistate modeling framework and developed model structure selection procedure to identify covariates with time-varying coefficients, time-independent coefficients, and null effects. The mental health status of Chinese residents aged 45 and older were analysed based on the China Health and Retirement Longitudinal Study (CHARLS). |
Title: | On Application of Semiparametric Methods |
Speaker: | Dr. Zhijian Li, Department of Statistics and Data Science, BNU-HKBU United International College, Zhuhai, China |
Time/Place: | 16:00 - 17:00 FSC1217 |
Abstract: | Generally, in the semiparametric model, we are only interested in the finite-dimensional parameters while taking the infinite-dimensional nonparametric components as nuisance parameters. The semiparametric theory provides a novel insight and deeper understanding of nonparametric models and gives rise to efficient estimators of the finite-dimensional parameters in these models. In this talk, we introduce the semiparametric theory and its application in nonparametric models and partially linear models. |
The Department has a distinguished record in teaching and research. A number of faculty members have been recipients of relevant awards.
Learn MoreDr S. Hon recevied the Early Career Award (21/22) from the Research Grants Council.
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