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Asymptotic normality of a Sobol index estimator in Gaussian process regression framework
Sensitivity analysis Gaussian process regression asymptotic normality stochas-tic simulators Sobol index
2013/6/14
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...
Asymptotic normality and efficiency of two Sobol index estimators
sensitivity analysis Sobol indices asymptotic efficiency asymptotic normality confidence intervals metamodelling surface response methodology
2013/4/28
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...
Asymptotic Normality of Estimates in Flexible Seasonal Time Series Model with Weak Dependent Error Terms
seasonal time series model local linear estimates consistency and asymptotic
2013/5/2
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...
Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values
Convergence asymptotic normality variational Bayesian approximations exponential family models missing values
2012/9/19
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...
Asymptotic normality of the optimal solution in multiresponse surface methodology
Asymptotic normality multiresponse surface optimisation sensitivity analysis mathematical programming
2012/9/19
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...
Asymptotic Normality of Maximum Likelihood and its Variational Approximation for Stochastic Blockmodels
network statistics stochastic blockmodeling, varia-tional methods maximum likelihood
2012/9/18
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...
Asymptotic Normality of Support Vector Machines for Classification and Regression
Nonparametric regression support vector machines asymptotic normality
2010/10/14
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...
Note on asymptotic normality of kernel density estimator for linear process under short-range dependence
asymptotic normality kernel density estimator linear process short-range dependence
2009/9/21
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
Asymptotic Normality Stochastic Epidemic Models
2009/9/17
Asymptotic Normality of Estimates for a Class of Stochastic Epidemic Models。
Asymptotic normality of the integrated square error of a density estimator in the convolution model
convolution density estimation nonparametric density estimation central limit theorem integrated squared error noisy observations
2009/2/23
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...