Let (X1, Y1),..., (Xn, Yn) be i.i.d. rv's and let m(x) = E(YX = x) be the regression curve of Y on X. A M-smoother mn(x) is a robust, nonlinear estimator of m(x), defined in analogy to robust M-estimators of location. In this paper the asymptotic maximal deviation sup0≤ t ≤ 1 |mn(t) -m(t)| is considered. The derived result allows the construction of a uniform confidence band for m(x)
A reasonable approach to robust regression estimation is minimizing a robust scale estimator of the...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
Let (X1; Y1), …, (Xn; Yn) be i.i.d. rvs and let v(x) be the unknown τ - expectile regression curve o...
Let (X1, Y1),..., (Xn, Yn) be i.i.d. rv's and let m(x) = E(YX = x) be the regression curve of Y on X...
AbstractLet (X1, Y1),…, (Xn, Yn) be i.i.d. rv's and let m(x) = E(Y|X = x) be the regression curve of...
AbstractLet (X, Y) have regression function m(x) = E(Y | X = x), and let X have a marginal density f...
Let (X, Y) have regression function m(x) = E(Y X = x), and let X have a marginal density f1(x). We c...
AbstractIn the linear model Xn × 1 = Cn × pθp × 1 + En × 1, Huber's theory of robust estimation of t...
AbstractWe discuss the asymptotic linearization of multivariate M-estimators, when the limit distrib...
In the present paper we construct asymptotic confidence bands in nonparametric regression. Our assum...
Asymmetry along with heteroscedasticity or contamination often occurs with the growth of data dimens...
We propose a class of robust estimates for multivariate linear models. Based on the approach of MM e...
AbstractWe investigate optimal bounded influence M-estimators in the general normal regression model...
The robustness properties of a regression estimate are throughly described by its maxbias curve. How...
AbstractThe asymptotic distribution of multivariate M-estimates is studied. It is shown that, in gen...
A reasonable approach to robust regression estimation is minimizing a robust scale estimator of the...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
Let (X1; Y1), …, (Xn; Yn) be i.i.d. rvs and let v(x) be the unknown τ - expectile regression curve o...
Let (X1, Y1),..., (Xn, Yn) be i.i.d. rv's and let m(x) = E(YX = x) be the regression curve of Y on X...
AbstractLet (X1, Y1),…, (Xn, Yn) be i.i.d. rv's and let m(x) = E(Y|X = x) be the regression curve of...
AbstractLet (X, Y) have regression function m(x) = E(Y | X = x), and let X have a marginal density f...
Let (X, Y) have regression function m(x) = E(Y X = x), and let X have a marginal density f1(x). We c...
AbstractIn the linear model Xn × 1 = Cn × pθp × 1 + En × 1, Huber's theory of robust estimation of t...
AbstractWe discuss the asymptotic linearization of multivariate M-estimators, when the limit distrib...
In the present paper we construct asymptotic confidence bands in nonparametric regression. Our assum...
Asymmetry along with heteroscedasticity or contamination often occurs with the growth of data dimens...
We propose a class of robust estimates for multivariate linear models. Based on the approach of MM e...
AbstractWe investigate optimal bounded influence M-estimators in the general normal regression model...
The robustness properties of a regression estimate are throughly described by its maxbias curve. How...
AbstractThe asymptotic distribution of multivariate M-estimates is studied. It is shown that, in gen...
A reasonable approach to robust regression estimation is minimizing a robust scale estimator of the...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
Let (X1; Y1), …, (Xn; Yn) be i.i.d. rvs and let v(x) be the unknown τ - expectile regression curve o...
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