title: A space-time skew-t model for threshold exceedances.
abstract: To assess the compliance of air quality regulations, the Environmental Protection Agency (EPA) must know if a site exceeds a pre-specified level. In the case of ozone, the level for compliance is fixed at 75 parts per billion, which is high, but not extreme at all locations. We present a new space-time model for threshold exceedances based on the skew-t process. Our method incorporates a random partition to permit long-distance asymptotic independence while allowing for sites that are near one another to be asymptotically dependent, and we incorporate thresholding to allow the tails of the data to speak for themselves. We also introduce a transformed AR(1) time-series to allow for temporal dependence. Finally, our model allows for high-dimensional Bayesian inference that is comparable in computation time to traditional geostatistical methods for large data sets. We apply our method to an ozone analysis for July 2005, and find that our model improves over both Gaussian and max-stable methods in terms of predicting exceedances of a high level.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A.
authors.forename: Samuel A
authors.initials: SA
authors.lastname: Morris
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Reich
authors.affiliation: Department of Statistics, Colorado State University, Fort Collins, Colorado, U.S.A.
authors.forename: Emeric
authors.initials: E
authors.lastname: Thibaud
authors.affiliation: Department of Statistics, Colorado State University, Fort Collins, Colorado, U.S.A.
authors.forename: Daniel
authors.initials: D
authors.lastname: Cooley
mesh_terms: D000393:Air Pollutants; D001499:Bayes Theorem; D015233:Models, Statistical; D016011:Normal Distribution; D010126:Ozone; D014481:United States; D014484:United States Environmental Protection Agency
chemical_list: D000393:Air Pollutants; D010126:Ozone
keywords:  Skew-t sep ; Extreme value analysis; Markov chain Monte Carlo; Random partition; Spatio-temporal modeling