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 …

Feature screening is a commonly used strategy to eliminate irrelevant features in high-
dimensional classification. When one encounters big datasets with both high dimensionality …

Modern statistical analysis often encounters datasets with large sizes. For these datasets,
conventional estimation methods can hardly be used immediately because practitioners …

This paper presents a selective review of statistical computation methods for massive data
analysis. A huge amount of statistical methods for massive data computation have been …

This paper studies the combination of random forest (RF) and classical statistical modeling.
We propose two algorithms: RF cluster+ ridge and RF regression+ ridge, in which the RF …

Predictive analytics involves the use of statistical models to make predictions; however, the
power of these techniques is hindered by ever-increasing quantities of data. The richness …

This paper studies the distributed conditional feature screening for massive data with
ultrahigh-dimensional features. Specifically, three distributed partial correlation feature …

Bagging is a useful method for large‐scale statistical analysis, especially when the
computing resources are very limited. We study here the asymptotic properties of bagging …