Bayesian Estimation for GEV-B-Spline Model

DOI: 10.4236/ojs.2013.32013   PDF   HTML   XML   4,403 Downloads   6,850 Views   Citations


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.

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B. Nasri, S. Adlouni and T. Ouarda, "Bayesian Estimation for GEV-B-Spline Model," Open Journal of Statistics, Vol. 3 No. 2, 2013, pp. 118-128. doi: 10.4236/ojs.2013.32013.

Conflicts of Interest

The authors declare no conflicts of interest.


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