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 …

We consider a measurement constrained supervised learning problem, that is,(i) full sample
of the predictors are given;(ii) the response observations are unavailable and expensive to …

Subdata selection methods provide flexible tradeoffs between computational complexity and
statistical efficiency in analyzing big data. In this work, we investigate a new algorithm for …

Subsampling methods aim to select a subsample as a surrogate for the observed sample.
Such methods have been used pervasively in large-scale data analytics, active learning …

Smoothing splines have been used pervasively in nonparametric regressions. However, the
computational burden of smoothing splines is significant when the sample size n is large …

Hierarchical data analysis is crucial in various fields for making discoveries. The linear
mixed model is often used for training hierarchical data, but its parameter estimation is …

Data subsampling has become widely recognized as a tool to overcome computational and
economic bottlenecks in analyzing massive datasets. We contribute to the development of …

In this paper, we consider the unified optimal subsampling estimation and inference on the
low-dimensional parameter of main interest in the presence of the nuisance parameter for …

Standard regression techniques model only the mean of the response variable. Quantile
regression (QR) is more powerful in that it depicts a comprehensive relationship between …

Subsampling techniques are efficient methods for handling big data. Quite a few optimal
sampling methods have been developed for parametric models in which the loss functions …