Dataset for: An Exponential-Gamma Mixture Model for Extreme Santa Ana Winds

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 sub-asymptotic, 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 multi-day occurrences of extreme Santa Ana winds over a range of high thresholds.