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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...
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 program opens with four days of tutorials that will provide an introduction to major themes of the entire program and the four workshops. The goal is to build a foundation for the participants of...
Increasingly large data sets are being ingested and produced by simulations. What experience from large-scale simulation is transferable to big data applications? Conversely, what new optimal algorith...
Big Data is the ocean of information we swim in every day-vast zetabytes of data flowing from our computers, mobile devices, and machine sensors. With the right solutions, organizations can dive into ...
Disasters are events that require multiple-agency responses, and resources beyond the capability of a community. Natural disasters put tremendous threats to the lives of people, in addition to economi...
The rapid growth of data starts to inflict a major impact in many fields such as computational mathematics, optimization, statistics, computer science, etc. Big data analysis is a new and exciting dir...
The rapid growth of data starts to inflict a major impact in many fields such as computational mathematics, optimization, statistics, computer science, etc. Big data analysis is a new and exciting dir...
Big data problems present great challenges to statisti-cal analyses, especially from the computational side. In this paper, we consider regression estimation of high-order mo-ments in big data problem...

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