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Joint quantile regression for spatial data

Publication ,  Journal Article
Chen, X; Tokdar, ST
Published in: Journal of the Royal Statistical Society. Series B: Statistical Methodology
September 1, 2021

Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust for any dependency between observation units, largely because such methods are not based upon a fully generative model of the data. For analysing spatially indexed data, we address this difficulty by generalizing the joint quantile regression model of Yang and Tokdar (Journal of the American Statistical Association, 2017, 112(519), 1107–1120) and characterizing spatial dependence via a Gaussian or t-copula process on the underlying quantile levels of the observation units. A Bayesian semiparametric approach is introduced to perform inference of model parameters and carry out spatial quantile smoothing. An effective model comparison criteria is provided, particularly for selecting between different model specifications of tail heaviness and tail dependence. Extensive simulation studies and two real applications to particulate matter concentration and wildfire risk are presented to illustrate substantial gains in inference quality, prediction accuracy and uncertainty quantification over existing alternatives.

Duke Scholars

Published In

Journal of the Royal Statistical Society. Series B: Statistical Methodology

DOI

EISSN

1467-9868

ISSN

1369-7412

Publication Date

September 1, 2021

Volume

83

Issue

4

Start / End Page

826 / 852

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0102 Applied Mathematics
 

Citation

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Chen, X., & Tokdar, S. T. (2021). Joint quantile regression for spatial data. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 83(4), 826–852. https://doi.org/10.1111/rssb.12467
Chen, X., and S. T. Tokdar. “Joint quantile regression for spatial data.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 83, no. 4 (September 1, 2021): 826–52. https://doi.org/10.1111/rssb.12467.
Chen X, Tokdar ST. Joint quantile regression for spatial data. Journal of the Royal Statistical Society Series B: Statistical Methodology. 2021 Sep 1;83(4):826–52.
Chen, X., and S. T. Tokdar. “Joint quantile regression for spatial data.” Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 83, no. 4, Sept. 2021, pp. 826–52. Scopus, doi:10.1111/rssb.12467.
Chen X, Tokdar ST. Joint quantile regression for spatial data. Journal of the Royal Statistical Society Series B: Statistical Methodology. 2021 Sep 1;83(4):826–852.
Journal cover image

Published In

Journal of the Royal Statistical Society. Series B: Statistical Methodology

DOI

EISSN

1467-9868

ISSN

1369-7412

Publication Date

September 1, 2021

Volume

83

Issue

4

Start / End Page

826 / 852

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0102 Applied Mathematics