Computing an ensemble of random fields using conditional simulation is ideal method for retrieving accurate estimates a field conditioned on available data and quantifying the uncertainty these realizations. Methods generating realizations, however, are computationally demanding, especially when numerous observed large domains. In this article, new, approximate approach applied that builds circ...

This paper proposes a semiparametric methodology for modeling multivariate and conditional distributions. We first build a multivariate distribution whose dependence structure is induced by a Gaussian copula and whose marginal distributions are estimated nonparametrically via mixtures of B-spline densities. The conditional distribution of a given variable is obtained in closed form from this mu...

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Abstract: Parameter estimation in a class of heteroscedastic time series models is investigated. The existence of conditional least-squares and conditional likelihood estimators is proved. Their consistency and their asymptotic normality are established. Kernel estimators of the noise’s density and its derivatives are defined and shown to be uniformly consistent. A simulation experiment conduct...