The screening of advanced materials coupled with the modeling of their quantitative
structural-activity relationships has recently become one of the hot and trending topics in …

Subsampling focuses on selecting a subsample that can efficiently sketch the information of
the original data in terms of statistical inference. It provides a powerful tool in big data …

Extraordinary amounts of data are being produced in many branches of science. Proven
statistical methods are no longer applicable with extraordinary large datasets due to …

This article presents a general sampling method, called granular-ball sampling (GBS), for
classification problems by introducing the idea of granular computing. The GBS method …

Distributed system has been widely used to solve massive data analysis tasks. This article
targets on quantile regression on distributed system with non-randomly distributed massive …

We investigate optimal subsampling for quantile regression. We derive the asymptotic
distribution of a general subsampling estimator and then derive two versions of optimal …

Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …

Nonuniform subsampling methods are effective to reduce computational burden and
maintain estimation efficiency for massive data. Existing methods mostly focus on …

In order to quickly approximate maximum likelihood estimators from massive data, this study
examines the optimal subsampling method under the A-optimality criterion (OSMAC) for …

In this paper, we propose improved estimation method for logistic regression based on
subsamples taken according the optimal subsampling probabilities developed in Wang et …