Joint Tracking of Multiple Quantiles Through Conditional Quantiles
Abstract
Estimation of quantiles is one of the most fundamental real-time analysis tasks. Most real-time data streams vary dynamically with time and incremental quantile estimators document state-of-the art performance to track quantiles of such data streams. However, most are not able to make joint estimates of multiple quantiles in a consistent manner, and estimates may violate the monotone property of quantiles. In this paper we propose the general concept of *conditional quantiles* that can extend incremental estimators to jointly track multiple quantiles. We apply the concept to propose two new estimators. Extensive experimental results, on both synthetic and real-life data, show that the new estimators clearly outperform legacy state-of-the-art joint quantile tracking algorithm and achieve faster adaptivity in dynamically varying data streams.
Cite
@article{arxiv.1902.05428,
title = {Joint Tracking of Multiple Quantiles Through Conditional Quantiles},
author = {Hugo Lewi Hammer and Anis Yazidi and Håvard Rue},
journal= {arXiv preprint arXiv:1902.05428},
year = {2019}
}
Comments
arXiv admin note: text overlap with arXiv:1901.04681