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 …

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 …

When the data are stored in a distributed manner, direct applications of traditional statistical
inference procedures are often prohibitive due to communication costs and privacy …

Distributed inference for quantile regression processes Page 1 The Annals of Statistics 2019,
Vol. 47, No. 3, 1634–1662 https://doi.org/10.1214/18-AOS1730 © Institute of Mathematical …

This paper studies distributed estimation and support recovery for high-dimensional linear
regression model with heavy-tailed noise. To deal with heavy-tailed noise whose variance …

Distributed linear regression by averaging Page 1 The Annals of Statistics 2021, Vol. 49, No. 2,
918–943 https://doi.org/10.1214/20-AOS1984 © Institute of Mathematical Statistics, 2021 …

The growing size of modern data brings many new challenges to existing statistical
inference methodologies and theories, and calls for the development of distributed …

In many areas, practitioners need to analyze large data sets that challenge conventional
single-machine computing. To scale up data analysis, distributed and parallel computing …

This article studies distributed estimation and inference for a general statistical problem with
a convex loss that could be nondifferentiable. For the purpose of efficient computation, we …

Background: Data from multiple organizations are crucial for advancing learning health
systems. However, ethical, legal, and social concerns may restrict the use of standard …