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Activity Number: 71
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #319162
Title: Censored Local Likelihood Inference for Modeling Nonstationarity in Spatial Extremes
Author(s): Daniela Castro* and Raphaƫl Huser
Companies: King Abdullah University of Science and Technology and KAUST
Keywords: Spatial extremes ; Factor copula models ; Local likelihood ; Threshold-based inference
Abstract:

Based on a recent paper, we propose to model spatial extremes using factor copula models. These models assume the presence of a common factor affecting the joint dependence of all measurements. When the common factor is exponentially distributed, the resulting copula is asymptotically equivalent to the H\"usler-Reiss copula; therefore, the so-called exponential factor model is suitable to model tail dependence. Under the assumption of local stationarity, the exponential factor model is used to model extreme measurements over high thresholds. Inference is performed using a censored local likelihood. Performance is assessed using simulation experiments, and illustrated using a daily rainfall dataset.


Authors who are presenting talks have a * after their name.

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