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 …

Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …

A statistical method is likely to be sub-optimal if the assumed model does not reflect the
structure of the data at hand. For this reason, it is important to perform model selection …

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 …

Subdata selection is necessary because of challenges arising from statistical analysis of big
data using limited computing resources. The existing work on subdata selection relies …

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 …

Subsampling or subdata selection is a useful approach in large-scale statistical learning.
Most existing studies focus on model-based subsampling methods which significantly …

Optimal sampling designs for multidimensional streaming time series with application to power
grid sensor data Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 4, 3195–3215 …