This paper deals with the Fisher-consistency, weak continuity and differentiability of estimating functionals corresponding to a class of both linear and nonlinear regression high breakdown M estimates, which includes S and MM estimates. A restricted type of differentiability, called weak differentiability, is defined, which suffices to prove the asymptotic normality of estimates based on the functionals. This approach allows to prove the consistency, asymptotic normality and qualitative robustness of M estimates under more general conditions than those required in standard approaches. In particular, we prove that regression MMestimates are asymptotically normal when the observations are φ-mixing.Facultad de Ciencias Exacta
The consistency of M-estimators in a very general setup is proven under weak assumptions. A one-dime...
In this paper we study the consistency and asymptotic normality properties of nonlinear least square...
AbstractLet (X1, Y1),…, (Xn, Yn) be i.i.d. rv's and let m(x) = E(Y|X = x) be the regression curve of...
This paper deals with the Fisher-consistency, weak continuity and differentiability of estimating fu...
Estimators which have locally uniform expansions are shown in this paper to be asymptotically equiva...
AbstractIt has been shown (Reeds, 1976, Ph.D. dissertation, Harvard University) that the remainder t...
AbstractThe asymptotic distribution of multivariate M-estimates is studied. It is shown that, in gen...
We consider the consistency and weak convergence of $S$-estimators in the linear regression model. S...
AbstractWe discuss the asymptotic linearization of multivariate M-estimators, when the limit distrib...
We investigate the asymptotic behavior of a nonparametric M-estimator of a regression function for s...
The asymptotic theory of estimators obtained from estimating functions is re-viewed and some new res...
Accepted for publication in Journal of Statistical Planning and InferenceInternational audienceThe a...
We provide sharp empirical estimates of expectation, variance and normal approximation for a class o...
This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic ...
This paper derives asymptotic normality of a class of M-estimators in the generalized autoregressive...
The consistency of M-estimators in a very general setup is proven under weak assumptions. A one-dime...
In this paper we study the consistency and asymptotic normality properties of nonlinear least square...
AbstractLet (X1, Y1),…, (Xn, Yn) be i.i.d. rv's and let m(x) = E(Y|X = x) be the regression curve of...
This paper deals with the Fisher-consistency, weak continuity and differentiability of estimating fu...
Estimators which have locally uniform expansions are shown in this paper to be asymptotically equiva...
AbstractIt has been shown (Reeds, 1976, Ph.D. dissertation, Harvard University) that the remainder t...
AbstractThe asymptotic distribution of multivariate M-estimates is studied. It is shown that, in gen...
We consider the consistency and weak convergence of $S$-estimators in the linear regression model. S...
AbstractWe discuss the asymptotic linearization of multivariate M-estimators, when the limit distrib...
We investigate the asymptotic behavior of a nonparametric M-estimator of a regression function for s...
The asymptotic theory of estimators obtained from estimating functions is re-viewed and some new res...
Accepted for publication in Journal of Statistical Planning and InferenceInternational audienceThe a...
We provide sharp empirical estimates of expectation, variance and normal approximation for a class o...
This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic ...
This paper derives asymptotic normality of a class of M-estimators in the generalized autoregressive...
The consistency of M-estimators in a very general setup is proven under weak assumptions. A one-dime...
In this paper we study the consistency and asymptotic normality properties of nonlinear least square...
AbstractLet (X1, Y1),…, (Xn, Yn) be i.i.d. rv's and let m(x) = E(Y|X = x) be the regression curve of...