Journal article
QUANTILE REGRESSION FOR SPATIALLY CORRELATED DATA: AN EMPIRICAL LIKELIHOOD APPROACH
Statistica Sinica, v 25(1), pp 261-274
01 Jan 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Quantile regression is a useful approach to modeling various aspects of conditional distributions. The Bayesian approach provides a natural framework for incorporating spatial correlation in a quantile regression model. This paper considers Bayesian spatial quantile regression using empirical likelihood as a working likelihood. The proposed approach inherits the merits of quantile regression in the sense that we can work with linear conditional quantile functions without having to assume a parametric form of the conditional distributions, and we allow each covariate to have differential impacts on different parts of the conditional distributions. Put into a Bayesian framework, this approach can incorporate spatial priors to smooth the conditional quantile functions across locations and across quantiles. We demonstrate both theoretically and empirically how the proposed approach can take advantage of spatial correlation to improve efficiency over the usual quantile regression estimators. An application to the statistical downscaling of daily precipitation in the Chicago area is given to illustrate the merit of our approach.
Metrics
Details
- Title
- QUANTILE REGRESSION FOR SPATIALLY CORRELATED DATA: AN EMPIRICAL LIKELIHOOD APPROACH
- Creators
- Yunwen Yang - Drexel UniversityXuming He - Statistics
- Publication Details
- Statistica Sinica, v 25(1), pp 261-274
- Publisher
- Statistica Sinica
- Number of pages
- 14
- Grant note
- 1307566 / Direct For Mathematical & Physical Scien; National Science Foundation (NSF); NSF - Directorate for Mathematical & Physical Sciences (MPS) R01GM080503-04 / NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA 11129101 / NNSFC; National Natural Science Foundation of China (NSFC) DMS-1237234 / NSF; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychiatry
- Web of Science ID
- WOS:000348969700016
- Scopus ID
- 2-s2.0-84959933477
- Other Identifier
- 991019173952104721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability