We consider the estimation of multivariate regression functions r(x1,...,xd) and their partial derivatives up to a total order p[greater-or-equal, slanted]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.Multivariate regression estimation Local polynomial fitting Associated processes Central limit theorems
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametr...
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...
AbstractWe consider the estimation of multivariate regression functions r(x1,…,xd) and their partial...
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 ...
International audienceIn this paper we study a local polynomial estimator of the regression function...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
Let (X-j, Y-j)(j=1)(n) be a realization of a bivariate jointly strictly stationary process. We consi...
Masry (1996b) provides estimation bias and variance expression for a general local polynomial kernel...
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...
While the additive model is a popular nonparametric regression method, many of its theoretical prope...
Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regressi...
summary:Local polynomials are used to construct estimators for the value $m(x_{0})$ of the regressio...
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametr...
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...
AbstractWe consider the estimation of multivariate regression functions r(x1,…,xd) and their partial...
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 ...
International audienceIn this paper we study a local polynomial estimator of the regression function...
We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mix...
Let (X-j, Y-j)(j=1)(n) be a realization of a bivariate jointly strictly stationary process. We consi...
Masry (1996b) provides estimation bias and variance expression for a general local polynomial kernel...
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...
While the additive model is a popular nonparametric regression method, many of its theoretical prope...
Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regressi...
summary:Local polynomials are used to construct estimators for the value $m(x_{0})$ of the regressio...
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametr...
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...