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Whether the 3D incompressible Euler equations can develop a finite-time singularity from smooth initial data is an outstanding open problem. In this talk, we will first review recent progress in singu...
Low rank matrix recovery is about reconstructing a low rank matrix from incomplete measurements. It arises frequently in many research areas of science and engineering, for example, machine learning, ...
Accurate HIV incidence estimation based on individual recent infection status (recent vs long-term infection) is important for monitoring the epidemic, targeting interventions to those at greatest ris...
We study multi-period inventory control systems in which managers face seasonal demands with unknown distributions and make inventory decisions based on past demand data. It can be shown that a data-d...
Solving multi-scale PDEs is difficult in high-dimensional and/or convection-dominant cases. The interacting particle methods (IPM) are shown to outperform solving PDEs directly. Examples include compu...
Nonlinear modelling of dynamic spatio-temporal network data is often a challenge with various issues, in particular due to irregularly observed locations and location-wide non-stationarity. In this pa...
This paper proposes a method to accelerate calculation for generalized linear models in big data. We separate the covariates of full model into several groups to build candidate models. In the process...
With noisy and asynchronous high-frequency data collected for an ultra-large number of assets, we estimate high-dimensional spot volatility matrices satisfying a low-rank plus sparse structure. A loca...
How to select the active variables which have significant impact on the event of interest is a very important and meaningful problem in the statistical analysis of ultrahigh-dimensional data. In many ...
Extremum seeking control(ESC)is a class of data-driven optimization algorithms which can drive the output of an unknown dynamic system to its optimal value with respect to some cost function by using ...
We consider a joint production-service planning problem with demand uncertainty, where the manufacturer aims to determine the portfolio of products with the associated service levels and the capacity ...
Linear mixed model is a popular and common modeling method in statistical analysis. It is computationally difficult to obtain parameter estimates in linear mixed model for big data. The current subsam...
The rank-tracking probability (RTP) is a useful statistical index for measuring the ``tracking ability'' of longitudinal disease risk factors in biomedical studies. A flexible nonparametric method for...
In this article, we propose a new joint modeling approach for recurrent event data with informative terminal event via   a gamma frailty by assigning a new type of double exponential Cox mod...

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