The rapid emergence of massive datasets in various fields poses a serious challenge to
traditional statistical methods. Meanwhile, it provides opportunities for researchers to …

Streaming data analysis has drawn much attention, where large amounts of data arrive in
streams. Because limited memory can only store a small batch of data, fast analysis without …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

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statistical methods are no longer applicable with extraordinary large datasets due to …

Data are distributed across different sites due to computing facility limitations or data privacy
considerations. Conventional centralized methods—those in which all datasets are stored …

Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts
latent principal factors that contribute to the most variation of the data. When data are stored …

Quantile regression under memory constraint Page 1 The Annals of Statistics 2019, Vol. 47,
No. 6, 3244–3273 https://doi.org/10.1214/18-AOS1777 © Institute of Mathematical Statistics …

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 …

Noisy matrix completion aims at estimating a low-rank matrix given only partial and
corrupted entries. Despite remarkable progress in designing efficient estimation algorithms …