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An exponential–gamma mixture model for extreme Santa Ana winds

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  • Gregory P. Bopp
  • Benjamin A. Shaby

Abstract

We analyze the behavior of extreme winds occurring in Southern California during the Santa Ana wind season using a latent mixture model. This mixture representation is formulated as a hierarchical Bayesian model and fit using Markov chain Monte Carlo. The two‐stage model results in generalized Pareto margins for exceedances and generates temporal dependence through a latent Markov process. This construction induces asymptotic independence in the response, while allowing for dependence at extreme, but subasymptotic, levels. We compare this model with a frequentist analogue where inference is performed via maximum pairwise likelihood. We use interval censoring to account for data quantization and estimate the extremal index and probabilities of multiday occurrences of extreme Santa Ana winds over a range of high thresholds.

Suggested Citation

  • Gregory P. Bopp & Benjamin A. Shaby, 2017. "An exponential–gamma mixture model for extreme Santa Ana winds," Environmetrics, John Wiley & Sons, Ltd., vol. 28(8), December.
    • Handle: RePEc:wly:envmet:v:28:y:2017:i:8:n:e2476
    DOI: 10.1002/env.2476
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    Cited by:

    1. Indranil Sahoo & Joseph Guinness & Brian J. Reich, 2023. "Estimating atmospheric motion winds from satellite image data using space‐time drift models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
    2. Jorge Castillo-Mateo & Miguel Lafuente & Jesús Asín & Ana C. Cebrián & Alan E. Gelfand & Jesús Abaurrea, 2022. "Spatial Modeling of Day-Within-Year Temperature Time Series: An Examination of Daily Maximum Temperatures in Aragón, Spain," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 487-505, September.
    3. Rishikesh Yadav & Raphaël Huser & Thomas Opitz, 2021. "Spatial hierarchical modeling of threshold exceedances using rate mixtures," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    4. Chang Yu & Ondrej Blaha & Michael Kane & Wei Wei & Denise Esserman & Daniel Zelterman, 2022. "Regression methods for the appearances of extremes in climate data," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.

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