%0 Journal Article %1 carreau2011spatial %A Carreau, Julie %A Girard, Stéphane %D 2011 %J Journal de la Société Française de Statistique %K Extremes Likelihood quantile spatial %N 3 %P 66-82 %T Spatial extreme quantile estimation using a weighted log-likelihood approach %V 152 %X We propose to estimate spatial extreme quantiles by a weighted log-likelihood approach. It is assumed that the conditional distribution of the variable of interest follows a generalized extreme-value distribution. The associated response surfaces are estimated thanks to the introduction of weights in the log-likelihood. These weights depend on the distance between the point of interest and the observations. The construction of a proper distance relies on the combination of a multidimensional scaling unfolding with a neural network regression. Our approach is illustrated both on simulated and real rainfall datasets.

@article{carreau2011spatial, abstract = {We propose to estimate spatial extreme quantiles by a weighted log-likelihood approach. It is assumed that the conditional distribution of the variable of interest follows a generalized extreme-value distribution. The associated response surfaces are estimated thanks to the introduction of weights in the log-likelihood. These weights depend on the distance between the point of interest and the observations. The construction of a proper distance relies on the combination of a multidimensional scaling unfolding with a neural network regression. Our approach is illustrated both on simulated and real rainfall datasets. }, added-at = {2013-03-05T10:17:09.000+0100}, author = {Carreau, Julie and Girard, Stéphane}, biburl = {https://www.bibsonomy.org/bibtex/273372bff6d32725274b9b2c8783f3fec/marsianus}, interhash = {efd99cb7e5b6aa3d1425957ef557f52e}, intrahash = {73372bff6d32725274b9b2c8783f3fec}, journal = {Journal de la Société Française de Statistique}, keywords = {Extremes Likelihood quantile spatial}, number = 3, pages = {66-82}, timestamp = {2013-03-05T10:17:09.000+0100}, title = {Spatial extreme quantile estimation using a weighted log-likelihood approach }, volume = 152, year = 2011 }