A posteriori residual error estimation of a cell-centered finite volume method

We present an a posteriori residual error estimator for the Laplace equation using a cell-centered finite volume method in the plane. For that purpose we associate to the approximated solution a kind of Morley interpolant. The error is then the difference between the exact solution and this Morley interpolant. The residual error estimator is based on the jump of normal and tangential derivatives of the Morley interpolant. The equivalence between the discrete H¹-seminorm of the error and the residual error estimator is proved. The proof of the upper error bound uses the Helmholtz decomposition of the broken gradient of the error and some quasi-orthogonality relations. To cite this article: S. Nicaise, C. R. Acad. Sci. Paris, Ser. I 338 (2004).     

A residual based a posteriori estimator for the reaction-diffusion problem

A residual based a posteriori estimator for the reaction-diffusion problem is introduced. We show that the estimator gives both an upper and a lower bound to error. Numerical results are presented. To cite this article: M. Juntunen, R. Stenberg, C. R. Acad. Sci. Paris, Ser. I 347 (2009).     

Nonparametric estimation of the density of the regression noise

This Note presents an estimator of the density of the error in a homoscedastic regression model, based on model selection methods, and propose a bound for the quadratic integrated risk. To cite this article: S. Plancade, C. R. Acad. Sci. Paris, Ser. I 346 (2008).     

Estimateur hiérarchique robuste pour un problème de perturbation singulière

In this Note, we show that a modified and simplified version of the estimator of Bank–Weiser can be used to define a robust a posteriori error estimator for singularly perturbed problem. We prove without comparison with a residual estimator or saturation assumption, the equivalence of the estimator with the error in the energy norm and the robusteness with respect to the diffusion coefficient. To cite this article: B. Achchab et al., C. R. Acad. Sci. Paris, Ser. I 336 (2003).     

Estimation suroptimale de la densité spectrale par projection adaptative : Application à l'estimation de l'ordre d'une moyenne mobile

We study an adaptive estimator of the spectral density by projection. We show that this estimator reaches a superoptimal rate on a dense set in the spectral densities class, and a quasi-optimal rate elsewhere. This set can be chosen by the Statistician, and the superoptimal speed is reached for integrated quadratic error and almost sure uniform convergence. As an application we obtain a consistent estimator of a moving average order. To cite this article: M. Souare, C. R. Acad. Sci. Paris, Ser. I 346 (2008).     

Estimateur a posteriori en norme L∞ pour les équations elliptiquesL∞-a posteriori error estimator for elliptic equations

In this Note, we show that modification of Bank–Wieser estimator introduce an L^∞-a posteriori error estimator for conforming and nonconforming methods. We prove, without saturation assumption nor comparison with residual estimators, the equivalence with the L^∞ error. To cite this article: A. Agouzal, C. R. Acad. Sci. Paris, Ser. I 334 (2002) 411–415.     

A posteriori boundary element error estimation

An a posteriori error estimator is presented for the boundary element method in a general framework. It is obtained by solving local residual problems for which a local concept is introduced to accommodate the fact that integral operators are nonlocal operators. The estimator is shown to have an upper and a lower bound by the constant multiples of the exact error in the energy norm for Symm's and hypersingular integral equations. Numerical results are also given to demonstrate the effectiveness of the estimator for these equations. It can be used for adaptive h,p, and hp methods.     

Estimation localement suroptimale et adaptative de la densitéLocally superoptimal and adaptive projection density estimators

We study a data-driven version of the density projection estimator in a general framework. We show that this estimator reaches a superoptimal rake on a dense set in the density class, and a quasi-optimal rake elsewhere. This set can be chosen by the statistician, and the superoptimal speed is reached for integrated quadratic error and almost sure uniform convergence. An adaptive version of the estimator is also considered. To cite this article: D. Bosq, C. R. Acad. Sci. Paris, Ser. I 334 (2002) 591–595.     

A posteriori error estimation for the dual mixed finite element method of the Stokes problem

This Note presents an a posteriori error estimator of residual type for the stationary Stokes problem using the dual mixed FEM. We prove lower and upper error bounds with the explicit dependence of the viscosity parameter and without any regularity assumption on the solution. To cite this article: M. Farhloul et al., C. R. Acad. Sci. Paris, Ser. I 339 (2004).     

A posteriori error estimator based on gradient recovery by averaging for discontinuous Galerkin methods

We consider some (anisotropic and piecewise constant) diffusion problems in domains of R², approximated by a discontinuous Galerkin method with polynomials of any fixed degree. We propose an a posteriori error estimator based on gradient recovery by averaging. It is shown that this estimator gives rise to an upper bound where the constant is one up to some additional terms that guarantee reliability. The lower bound is also established. Moreover these additional terms are negligible when the recovered gradient is superconvergent. The reliability and efficiency of the proposed estimator is confirmed by some numerical tests.     

Low cost a posteriori error estimators for an augmented mixed FEM in linear elasticity

We consider an augmented mixed finite element method applied to the linear elasticity problem and derive a posteriori error estimators that are simpler and easier to implement than the ones available in the literature. In the case of homogeneous Dirichlet boundary conditions, the new a posteriori error estimator is reliable and locally efficient, whereas for non-homogeneous Dirichlet boundary conditions, we derive an a posteriori error estimator that is reliable and satisfies a quasi-efficiency bound. Numerical experiments illustrate the performance of the corresponding adaptive algorithms and support the theoretical results.     

Generalized ridge regression,least squares with stochastic prior information,and Bayesian estimators

The ridge estimator of the usual linear model is generalized by the introduction of an a priori vector r and an associated positive semidefinite matrix S. It is then shown that the generalized ridge estimator can be justified in two ways: (a) by the minimization of the residual sum of squares subject to a constraint on the length, in the metric S, of the vector of differences between r and the estimated linear model coefficients, (b) by incorporating prior knowledge, r playing the role of the vector of means and S proportional to the precision matrix. Both a Bayesian and an Aitken generalized least squares frameworks are used for the latter. The properties of the new estimator are derived and compared to the ordinary least squares estimator. The new method is illustrated with different assumptions on the form of the S matrix.     

A posteriori error estimate for a one-dimensional pollution problem in porous media

We are interested in the discretization of a time-dependent pollution problem modeling the mass transfer of contaminant in porous media, by the implicit Euler scheme in time and vertex-centered finite volumes in space. The error estimator consists of two types of computable error indicators, the first one being linked to the time discretization and the second one to the space discretization. To cite this article: R. Aboulaich et al., C. R. Acad. Sci. Paris, Ser. I 347 (2009).     

Asymptotic bounds for the expected L error of a multivariate kernel density estimator

The kernel estimator of a multivariate probability density function is studied. An asymptotic upper bound for the expected L¹ error of the estimator is derived. An asymptotic lower bound result and a formula for the exact asymptotic error are also given. The goodness of the smoothing parameter value derived by minimizing an explicit upper bound is examined in numerical simulations that consist of two different experiments. First, the L¹ error is estimated using numerical integration and, second, the effect of the choice of the smoothing parameter in discrimination tasks is studied.     

Simple regression in view of elliptical models

For the simple linear model Y = θ1 + βx + ? where the error vector follows the elliptically contoured distribution, we consider the unrestricted, restricted, preliminary test and shrinkage estimators for the intercept parameter, θ when it is suspected that the slope parameter β may be β_o. The exact bias and MSE expressions are derived and the mean-square relative efficiency is taken to determine the relative dominance properties of the proposed estimators in comparison. In the continuation, the optimal level of significance of the preliminary test estimator is tabulated and some graphical result are also displayed.     

Efficiency of a Liu-type estimator in semiparametric regression models

In this paper we consider the semiparametric regression model, y=Xβ+f+ε. Recently, Hu [11] proposed ridge regression estimator in a semiparametric regression model. We introduce a Liu-type (combined ridge-Stein) estimator (LTE) in a semiparametric regression model. Firstly, Liu-type estimators of both β and f are attained without a restrained design matrix. Secondly, the LTE estimator of β is compared with the two-step estimator in terms of the mean square error. We describe the almost unbiased Liu-type estimator in semiparametric regression models. The almost unbiased Liu-type estimator is compared with the Liu-type estimator in terms of the mean squared error matrix. A numerical example is provided to show the performance of the estimators.     

Estimation spline de quantiles conditionnels pour variables explicatives fonctionnelles

This Note deals with a linear model of regression on quantiles with the explanatory variable taking values in some functional space and a scalar response. We propose a spline estimator of the functional coefficient that minimizes a penalized L¹ type criterion (the penalization is of primary importance to get existence and convergence of the estimator), then we study the asymptotic behaviour of this estimator. To cite this article: H. Cardot et al., C. R. Acad. Sci. Paris, Ser. I 339 (2004).     

A posteriori L2 error estimation on anisotropic tetrahedral finite element meshes

A new a posteriori L₂ norm error estimator is proposed for thePoisson equation. The error estimator can be applied to anisotropictetrahedral or triangular finite element meshes. The estimatoris rigorously analysed for Dirichlet and Neumann boundary conditions. The lower error bound relies on specifically designed anisotropicbubble functions and the corresponding inverse inequalities.The upper error bound utilizes non-standard anisotropic interpolationestimates. Its proof requires H² regularity of the Poisson problem,and its quality depends on how good the anisotropic mesh resolvesthe anisotropy of the problem. This is measured by a so-called‘matching function’. A numerical example supports the anisotropic error analysis.     

On near neighbour estimates of a multivariate density

A recent paper by Mack and Rosenblatt (J. Multivar. Anal.9 (1979), 1–15) has shown that near neighbour estimators of a density may perform more poorly than other kernel-type estimators, particularly for x values in the tail of a distribution. In order to overcome the difficulties discovered by Mack and Rosenblatt, a generalized type of near neighbour estimator is proposed. Here the window size, or bandwidth, is chosen as a function of near neighbour distances, rather than actually equal to one of the distances. Two forms for this function are suggested and it is proved that for large samples the resulting estimator does not suffer the drawbacks of the usual near neighbour estimator.     

Residual error estimators for the time-dependent Stokes equations

We present a posteriori residual error estimators for the approximate time-dependent Stokes model Chorin–Temam (Chorin, Math. Comp. 23 (1969) 341–353) projection scheme using a conforming finite element discretization. We prove a global upper bound and local lower bounds for the error on the velocity field only. To cite this article: N. Kharrat, Z. Mghazli, C. R. Acad. Sci. Paris, Ser. I 340 (2005).