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S. El Adlouni, A. C. Favre and B. Bobée, “Comparison of Methodologies to Assess the Convergence of Markov Chain Monte Carlo Methods,” Computational Statistics and Data Analysis, Vol. 50, No. 10, 2006, pp. 2685-2701. doi:10.1016/j.csda.2005.04.018

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• TITLE: Bayesian Estimation for GEV-B-Spline Model

  AUTHORS: Bouchra Nasri, Salaheddine El Adlouni, Taha B. M. J. Ouarda

  KEYWORDS: GEV; Bayesien; B-Spline; Nonlinearity; Covariate; Non-Stationarity

  JOURNAL NAME: Open Journal of Statistics, Vol.3 No.2, April 26, 2013

  ABSTRACT: The stationarity hypothesis is essential in hydrological frequency analysis and statistical inference. This assumption is often not fulfilled for large observed datasets, especially in the case of hydro-climatic variables. The Generalized Extreme Value distribution with covariates allows to model data in the presence of non-stationarity and/or dependence on covariates. Linear and non-linear dependence structures have been proposed with the corresponding fitting approach. The objective of the present study is to develop the GEV model with B-Spline in a Bayesian framework. A Markov Chain Monte Carlo (MCMC) algorithm has been developed to estimate quantiles and their posterior distributions. The methods are tested and illustrated using simulated data and applied to meteorological data. Results indicate the better performance of the proposed Bayesian method for rainfall quantile estimation according to BIAS and RMSE criteria especially for high return period events.