Open Access
March 2015 Modeling for seasonal marked point processes: An analysis of evolving hurricane occurrences
Sai Xiao, Athanasios Kottas, Bruno Sansó
Ann. Appl. Stat. 9(1): 353-382 (March 2015). DOI: 10.1214/14-AOAS796


Seasonal point processes refer to stochastic models for random events which are only observed in a given season. We develop nonparametric Bayesian methodology to study the dynamic evolution of a seasonal marked point process intensity. We assume the point process is a nonhomogeneous Poisson process and propose a nonparametric mixture of beta densities to model dynamically evolving temporal Poisson process intensities. Dependence structure is built through a dependent Dirichlet process prior for the seasonally-varying mixing distributions. We extend the nonparametric model to incorporate time-varying marks, resulting in flexible inference for both the seasonal point process intensity and for the conditional mark distribution. The motivating application involves the analysis of hurricane landfalls with reported damages along the U.S. Gulf and Atlantic coasts from 1900 to 2010. We focus on studying the evolution of the intensity of the process of hurricane landfall occurrences, and the respective maximum wind speed and associated damages. Our results indicate an increase in the number of hurricane landfall occurrences and a decrease in the median maximum wind speed at the peak of the season. Introducing standardized damage as a mark, such that reported damages are comparable both in time and space, we find that there is no significant rising trend in hurricane damages over time.


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Sai Xiao. Athanasios Kottas. Bruno Sansó. "Modeling for seasonal marked point processes: An analysis of evolving hurricane occurrences." Ann. Appl. Stat. 9 (1) 353 - 382, March 2015.


Published: March 2015
First available in Project Euclid: 28 April 2015

zbMATH: 06446572
MathSciNet: MR3341119
Digital Object Identifier: 10.1214/14-AOAS796

Keywords: Bayesian nonparametrics , dependent Dirichlet process , hurricane intensity , marked Poisson process , Markov chain Monte Carlo , risk assessment

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.9 • No. 1 • March 2015
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