Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation

Handle URI:
http://hdl.handle.net/10754/621492
Title:
Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation
Authors:
Sun, Ying ( 0000-0001-6703-4270 ) ; Wang, Huixia J.; Fuentes, Montserrat
Abstract:
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Sun Y, Wang HJ, Fuentes M (2016) Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation. Technometrics 58: 127–137. Available: http://dx.doi.org/10.1080/00401706.2015.1017115.
Publisher:
Informa UK Limited
Journal:
Technometrics
Issue Date:
1-Sep-2015
DOI:
10.1080/00401706.2015.1017115
Type:
Article
ISSN:
0040-1706; 1537-2723
Sponsors:
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.
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSun, Yingen
dc.contributor.authorWang, Huixia J.en
dc.contributor.authorFuentes, Montserraten
dc.date.accessioned2016-11-03T08:30:41Z-
dc.date.available2016-11-03T08:30:41Z-
dc.date.issued2015-09-01en
dc.identifier.citationSun Y, Wang HJ, Fuentes M (2016) Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation. Technometrics 58: 127–137. Available: http://dx.doi.org/10.1080/00401706.2015.1017115.en
dc.identifier.issn0040-1706en
dc.identifier.issn1537-2723en
dc.identifier.doi10.1080/00401706.2015.1017115en
dc.identifier.urihttp://hdl.handle.net/10754/621492-
dc.description.abstractQuantile 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.en
dc.description.sponsorshipThe 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.en
dc.publisherInforma UK Limiteden
dc.subjectFused adaptive Lassoen
dc.titleFused Adaptive Lasso for Spatial and Temporal Quantile Function Estimationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalTechnometricsen
dc.contributor.institutionDepartment of Statistics, George Washington University, Washington, DC, United Statesen
dc.contributor.institutionDepartment of Statistics, North Carolina State University, Raleigh, NC, United Statesen
kaust.authorSun, Yingen
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