Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation

Ying Sun, Huixia J. Wang, Montserrat Fuentes

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Quantile functions are important in characterizing the entire probability distribution of a random variable, especially when the tail of a skewed distribution is of interest. This article introduces new quantile function estimators for spatial and temporal data with a fused adaptive Lasso penalty to accommodate the dependence in space and time. This method penalizes the difference among neighboring quantiles, hence it is desirable for applications with features ordered in time or space without replicated observations. The theoretical properties are investigated and the performances of the proposed methods are evaluated by simulations. The proposed method is applied to particulate matter (PM) data from the Community Multiscale Air Quality (CMAQ) model to characterize the upper quantiles, which are crucial for studying spatial association between PM concentrations and adverse human health effects. © 2016 American Statistical Association and the American Society for Quality.
Original languageEnglish (US)
Pages (from-to)127-137
Number of pages11
Issue number1
StatePublished - Feb 22 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors thank the valuable suggestions from the associate editor and referees for contributing to the noticeable improvement of the article. This research was partially supported by the US National Science Foundation (NSF) grants DMS-1106862, 1106974, and 1107046, the NSF CAREER award DMS-1149355, and the STATMOS research network on Statistical Methods in Oceanic and Atmospheric Sciences.


Dive into the research topics of 'Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation'. Together they form a unique fingerprint.

Cite this