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Stochastic simulators such as Monte-Carlo estimators are widely used in science and engineering to study physical systems through their probabilistic representation. Global sensitivity analysis aims t...
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variabilit...
In this paper we considered a general seasonal time series model with K-dependent and \rambda-dependent errors, which are new concepts of dependence. In this model we derived consistency and asymptoti...
We study the properties of variational Bayes approximations for exponential family mod-els with missing values. It is shown that the iterative algorithm for obtaining the varia-tional Bayesian estimat...
In this work is obtained an explicit form for the perturbation effect on the matrix of regression coefficients on the optimal solution in multiresponse surface methodology. Then, the sensitivity analy...
Variational methods for parameter estimation are an activere-search area, potentially offering computationally tractable heuristics with theoretical performance bounds. We build on recent work that ap...
In nonparametric classification and regression problems, support vector machines (SVMs) attract much attention in theoretical and in applied statistics. In an abstract sense, SVMs can be seen as regu...
Wc mnf;i&r the paablem of density estimation for m a one-sided linear prosees X, = zt _ , a, Z, , with i.id square iategra- Me kovatims - We prove that under weak contritions on (ai)&, which imply ...
Asymptotic Normality of Estimates for a Class of Stochastic Epidemic Models。
In this paper we consider a kernel estimator of a density in a convolution model and give a central limit theorem for its integrated square error (ISE). The kernel estimator is rather classical in min...

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