AbstractWe consider the estimation of multivariate regression functions r(x1,…,xd) and their partial derivatives up to a total order p⩾1 using high-order local polynomial fitting. The processes {Yi,Xi} are assumed to be (jointly) associated. Joint asymptotic normality is established for the estimates of the regression function r and all its partial derivatives up to the total order p. Expressions for the bias and variance/covariance matrix (of the asymptotic distribution) are given
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
In this paper, we study the nonparametric estimation of the regression function and its derivatives...
AbstractWe consider the estimation of multivariate regression functions r(x1,…,xd) and their partial...
We consider the estimation of multivariate regression functions r(x1,...,xd) and their partial deriv...
AbstractWe consider the estimation of the multivariate regression function m(x1, …, xd) = E[ψ(Yd)|X1...
AbstractNonparametric regression estimator based on locally weighted least squares fitting has been ...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
Local polynomial fitting has many exciting statistical properties which where established under i.i....
Consider the fixed regression model with random observation error that follows an AR(1) correlation...
The asymptotic bias and variance of a general class of local polynomial estimators of M-regression f...
International audienceIn this paper we study a local polynomial estimator of the regression function...
summary:Local polynomials are used to construct estimators for the value $m(x_{0})$ of the regressio...
Let (X-j, Y-j)(j=1)(n) be a realization of a bivariate jointly strictly stationary process. We consi...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
In this paper, we study the nonparametric estimation of the regression function and its derivatives...
AbstractWe consider the estimation of multivariate regression functions r(x1,…,xd) and their partial...
We consider the estimation of multivariate regression functions r(x1,...,xd) and their partial deriv...
AbstractWe consider the estimation of the multivariate regression function m(x1, …, xd) = E[ψ(Yd)|X1...
AbstractNonparametric regression estimator based on locally weighted least squares fitting has been ...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
Local polynomial fitting has many exciting statistical properties which where established under i.i....
Consider the fixed regression model with random observation error that follows an AR(1) correlation...
The asymptotic bias and variance of a general class of local polynomial estimators of M-regression f...
International audienceIn this paper we study a local polynomial estimator of the regression function...
summary:Local polynomials are used to construct estimators for the value $m(x_{0})$ of the regressio...
Let (X-j, Y-j)(j=1)(n) be a realization of a bivariate jointly strictly stationary process. We consi...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
In this paper, we study the nonparametric estimation of the regression function and its derivatives...