Distributed Computing and Inference for Big Data

L Zhou, Z Gong, P Xiang - Annual Review of Statistics and Its …, 2023 - annualreviews.org
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

X Chen, W Liu, Y Zhang - 2019 - projecteuclid.org
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 …

Communication-efficient accurate statistical estimation

J Fan, Y Guo, K Wang - Journal of the American Statistical …, 2023 - Taylor & Francis
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

S Volgushev, SK Chao, G Cheng - 2019 - projecteuclid.org
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 …

Distributed high-dimensional regression under a quantile loss function

X Chen, W Liu, X Mao, Z Yang - Journal of Machine Learning Research, 2020 - jmlr.org
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

E Dobriban, Y Sheng - 2021 - projecteuclid.org
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 …

Distributed inference for linear support vector machine

X Wang, Z Yang, X Chen, W Liu - Journal of machine learning research, 2019 - jmlr.org
The growing size of modern data brings many new challenges to existing statistical
inference methodologies and theories, and calls for the development of distributed …

Wonder: Weighted one-shot distributed ridge regression in high dimensions

E Dobriban, Y Sheng - Journal of Machine Learning Research, 2020 - jmlr.org
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 …

First-order newton-type estimator for distributed estimation and inference

X Chen, W Liu, Y Zhang - Journal of the American Statistical …, 2022 - Taylor & Francis
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 …

[HTML][HTML] Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics

FC Lemyre, S Lévesque, MP Domingue… - JMIR Medical …, 2024 - medinform.jmir.org
Background: Data from multiple organizations are crucial for advancing learning health
systems. However, ethical, legal, and social concerns may restrict the use of standard …