Journal article
Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation
Technometrics, Vol.58(1), pp.127-137
01/02/2016
DOI: 10.1080/00401706.2015.1017115
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.
Details
- Title: Subtitle
- Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation
- Creators
- Ying Sun - CEMSE Division, King Abdullah University of Science and TechnologyHuixia J Wang - Department of Statistics, George Washington UniversityMontserrat Fuentes - Department of Statistics, North Carolina State University
- Resource Type
- Journal article
- Publication Details
- Technometrics, Vol.58(1), pp.127-137
- Publisher
- Taylor & Francis
- DOI
- 10.1080/00401706.2015.1017115
- ISSN
- 0040-1706
- eISSN
- 1537-2723
- Language
- English
- Date published
- 01/02/2016
- Academic Unit
- Statistics and Actuarial Science; President; Biostatistics
- Record Identifier
- 9984065772802771
Metrics
21 Record Views