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Article Dans Une Revue Reliability Engineering and System Safety Année : 2003

Improving extremal fit: a Bayesian regularization procedure

Résumé

In structural reliability, special attention is devoted to model distribution tails. The distributions are required to fit the upper observations and provide a picture of the tail above the maximal observation. Goodness-of-fit tests can be constructed to check this tail fit. Then what can we do with distributions having a good central fit and a bad extremal fit? We propose a regularization procedure. It is based on Bayesian tools and takes into account the opinion of experts. Predictive distributions are proposed as model distributions. We numerically investigate this method on normal, lognormal, exponential, gamma and Weibull distributions.

Dates et versions

hal-00806252 , version 1 (29-03-2013)

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Catherine Trottier, Myriam Garrido, Jean Diebolt. Improving extremal fit: a Bayesian regularization procedure. Reliability Engineering and System Safety, 2003, 82 (1), pp.21-31. ⟨10.1016/S0951-8320(03)00096-6⟩. ⟨hal-00806252⟩
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