Clouded Representatives and Score Matching for Distributed Quantile Regression
Abstract
Quantile regression is widely used in high-stakes applications where the data are distributed across many sites and the individual records cannot be shared. The existing distributed methods for quantile regression typically require homogeneous sites, large sites, or synchronous communication, which are rarely all satisfied in practice. In this paper, we propose a new method named cloudqr. Each site summarizes its observations by a few Gaussian clouds, each given by the size, center, and covariance of a group of observations, such that the clouds match the score of the raw data exactly. A central server then fits the quantile regression on the clouds from all sites. We establish the convergence of cloudqr under mild conditions. With simulation studies and two real-world applications, we demonstrate the superior performance of cloudqr over the existing distributed methods.
Related
- Distributed Selective Inference for Quantile Regression
- Heterogeneity-calibrated Byzantine-robust distributed composite quantile regression
- Quantile-Only Regression: Inference and Partial Identification under Quantile Disclosure
- A New Framework for Estimation of Unconditional Quantile Treatment Effects: The Residualized Quantile Regression (RQR) Model
- Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift
BibTeX
@misc{li2026clouded,
title = {{Clouded Representatives and Score Matching for Distributed Quantile Regression}},
author = {Keren Li and Boxiang Wang},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.07273},
eprint = {2611.07273},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.07273},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Clouded Representatives and Score Matching for Distributed Quantile Regression
AU - Keren Li
AU - Boxiang Wang
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.07273
UR - https://arxiv.org/abs/2611.07273
AB - Quantile regression is widely used in high-stakes applications where the data are distributed across many sites and the individual records cannot be shared. The existing distributed methods for quantile regression typically require homogeneous sites, large sites, or synchronous communication, which are rarely all satisfied in practice. In this paper, we propose a new method named cloudqr. Each site summarizes its observations by a few Gaussian clouds, each given by the size, center, and covariance of a group of observations, such that the clouds match the score of the raw data exactly. A central server then fits the quantile regression on the clouds from all sites. We establish the convergence of cloudqr under mild conditions. With simulation studies and two real-world applications, we demonstrate the superior performance of cloudqr over the existing distributed methods.
ER -