NDLI: A Hierarchical Model for Serially-Dependent Extremes: A Study of Heat Waves in the Western US

Content Provider	Springer Nature Link
Author	Reich, Brian J. Shaby, Benjamin A. Cooley, Daniel
Copyright Year	2013
Abstract	Heat waves take a major toll on human populations, with negative impacts on the economy, agriculture, and human health. As a result, there is great interest in studying the changes over time in the probability and magnitude of heat waves. In this paper we propose a hierarchical Bayesian model for serially-dependent extreme temperatures. We assume the marginal temperature distribution follows the generalized Pareto distribution (GPD) above a location-specific threshold, and capture dependence between subsequent days using a transformed max-stable process. Our model allows both the parameters in the marginal GPD and the temporal dependence function to change over time. This allows Bayesian inference on the change in likelihood of a heat wave. We apply this methodology to daily high temperatures in nine cities in the western US for 1979–2010. Our analysis reveals increases in the probability of a heat wave in several US cities. This article has supplementary material online.
Starting Page	119
Ending Page	135
Page Count	17
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	19
Issue Number	1
e-ISSN	15372693
Language	English
Publisher	Springer US
Publisher Date	2013-09-26
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian modeling Climate change Generalized Pareto distribution Markov model Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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