Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes

Castro-Camilo, D. and Huser, R. (2020) Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes. Journal of the American Statistical Association, 115(531), pp. 1037-1054. (doi: 10.1080/01621459.2019.1647842)

Abstract

To disentangle the complex nonstationary dependence structure of precipitation extremes over the entire contiguous United States (U.S.), we propose a flexible local approach based on factor copula models. Our subasymptotic spatial modeling framework yields nontrivial tail dependence structures, with a weakening dependence strength as events become more extreme; a feature commonly observed with precipitation data but not accounted for in classical asymptotic extreme-value models. To estimate the local extremal behavior, we fit the proposed model in small regional neighborhoods to high threshold exceedances, under the assumption of local stationarity, which allows us to gain in flexibility. By adopting a local censored likelihood approach, we make inference on a fine spatial grid, and we perform local estimation by taking advantage of distributed computing resources and the embarrassingly parallel nature of this estimation procedure. The local model is efficiently fitted at all grid points, and uncertainty is measured using a block bootstrap procedure. We carry out an extensive simulation study to show that our approach can adequately capture complex, nonstationary dependencies, in addition, our study of U.S. winter precipitation data reveals interesting differences in local tail structures over space, which has important implications on regional risk assessment of extreme precipitation events. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

• Item Type: Articles
• Additional Information: This publication is based upon work supported by KAUST Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3434.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Castro-Camilo, Dr Daniela
• Authors: Castro-Camilo, D., and Huser, R.
• College/School: College of Science and Engineering > School of Mathematics and Statistics
• Journal Name: Journal of the American Statistical Association
• Publisher: Taylor & Francis
• ISSN: 0162-1459
• ISSN (Online): 1537-274X
• Published Online: 25 July 2019
• Copyright Holders: Copyright © 2019 American Statistical Association
• First Published: First published in Journal of the American Statistical Association 115(531):1037-1054
• Publisher Policy: Reproduced in accordance with the publisher copyright policy