In this paper we define a robust conditional location functional without requiring any moment condition. We apply the nonparametric proposals considered by C. Stone (Ann. Statist. 5 (1977), 595-645) to this functional equation in order to obtain strongly consistent, robust nonparametric estimates of the regression function. We give some examples by using nearest neighbor weights or weights based on kernel methods under no assumptions whatsoever on the probability measure of the vector (X,Y). We also derive strong convergence rates and the asymptotic distribution of the proposed estimates.Robust estimation nonparametric regression nearest neighbor rules kernel estimates
Under quite mild conditions on K-n, the strong consistency is proved for the nearest neighbor densit...
The robustness properties of conditional normal-theory procedures of inference for a location parame...
This paper studies robust estimation of multivariate regression model using kernel weighted local li...
AbstractIn this paper we define a robust conditional location functional without requiring any momen...
AbstractA robust estimator of the regression function is proposed combining kernel methods as introd...
[[abstract]]The bias of kernel methods based on local constant fits can have an adverse effect when ...
Nonparametric regression methods provide an alternative approach to parametric estimation that requi...
Consider the problem of estimating the mean function underlying a set of noisy data. Least squares i...
Consider a stationary time series (Xt, Yt), t = 0, ±1, … with Xt being Rd-valued and Yt real-valued....
For a well-known class of nonparametric regression function estimators of nearest neighbor type the ...
AbstractFor a well-known class of nonparametric regression function estimators of nearest neighbor t...
AbstractConsider a stationary time series (Xt, Yt), t = 0, ±1, … with Xt being Rd-valued and Yt real...
Let y, ,..., Yn denote independent real-valued observations, each distributed accord-ing to a densit...
Nonparametric procedures use weak assumptions such as continuity of the distribution so that they ar...
Local kernel estimates and B-spline estimates are considered in the nonparametric regression and the...
Under quite mild conditions on K-n, the strong consistency is proved for the nearest neighbor densit...
The robustness properties of conditional normal-theory procedures of inference for a location parame...
This paper studies robust estimation of multivariate regression model using kernel weighted local li...
AbstractIn this paper we define a robust conditional location functional without requiring any momen...
AbstractA robust estimator of the regression function is proposed combining kernel methods as introd...
[[abstract]]The bias of kernel methods based on local constant fits can have an adverse effect when ...
Nonparametric regression methods provide an alternative approach to parametric estimation that requi...
Consider the problem of estimating the mean function underlying a set of noisy data. Least squares i...
Consider a stationary time series (Xt, Yt), t = 0, ±1, … with Xt being Rd-valued and Yt real-valued....
For a well-known class of nonparametric regression function estimators of nearest neighbor type the ...
AbstractFor a well-known class of nonparametric regression function estimators of nearest neighbor t...
AbstractConsider a stationary time series (Xt, Yt), t = 0, ±1, … with Xt being Rd-valued and Yt real...
Let y, ,..., Yn denote independent real-valued observations, each distributed accord-ing to a densit...
Nonparametric procedures use weak assumptions such as continuity of the distribution so that they ar...
Local kernel estimates and B-spline estimates are considered in the nonparametric regression and the...
Under quite mild conditions on K-n, the strong consistency is proved for the nearest neighbor densit...
The robustness properties of conditional normal-theory procedures of inference for a location parame...
This paper studies robust estimation of multivariate regression model using kernel weighted local li...