Add to ORKGIn parametric regression problems, estimation of the parameter of interest is typically achieved via the solution of a set of unbiased estimating equations. We are interested in problems where in addition to this parameter, the estimating equations consist of an unknown nuisance function which does not depend on the parameter. We study the effects of using a plug-in nonparametric estimator of the nuisance function (for example, a local--linear regression estimator) on the estimability of the parameter. In particular, we specify conditions on the functional estimator which ensure that the parametric rate of consistency for estimating the parameter of interest is preserved, and we give a general asymptotic covariance formula. We apply this th...
In this article, we propose to estimate the regression parameters in a semiparametric generalized li...
We consider the partially linear model relating a response Y to predictors (X,T) with mean function ...
We consider the semiparametric regressionXtβ+φ(Z) where β and φ(·) are unknown slope coefficient vec...
In parametric regression problems estimation of the parameter of interest is typically achieved via...
In parametric regression problems, estimation of the parameter of interest is typically achieved via...
We use ideas from estimating function theory to derive new, simply computed consistent covariance ma...
We are concerned with the asymptotic theory of semiparametric estimation equations. We are dealing w...
This note considers a puzzling phenomenon that is observed in some semiparametric estimation problem...
We use ideas from estimating function theory to derive new simply computed consistent covariance ma...
Vita.We develop methodology for the estimation of regression parameters in models where one of the ...
We consider the semiparametric regression X t +(Z) where and (℗ʺ) are unknown slope coefficient vect...
We consider estimation of linear functionals of the error distribution for two regression models: pa...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
Summary. Motivated by an analysis of a real data set in ecology, we consider a class of partially no...
Nonparametric and semiparametric regression models are useful statistical regression models to disco...
In this article, we propose to estimate the regression parameters in a semiparametric generalized li...
We consider the partially linear model relating a response Y to predictors (X,T) with mean function ...
We consider the semiparametric regressionXtβ+φ(Z) where β and φ(·) are unknown slope coefficient vec...
In parametric regression problems estimation of the parameter of interest is typically achieved via...
In parametric regression problems, estimation of the parameter of interest is typically achieved via...
We use ideas from estimating function theory to derive new, simply computed consistent covariance ma...
We are concerned with the asymptotic theory of semiparametric estimation equations. We are dealing w...
This note considers a puzzling phenomenon that is observed in some semiparametric estimation problem...
We use ideas from estimating function theory to derive new simply computed consistent covariance ma...
Vita.We develop methodology for the estimation of regression parameters in models where one of the ...
We consider the semiparametric regression X t +(Z) where and (℗ʺ) are unknown slope coefficient vect...
We consider estimation of linear functionals of the error distribution for two regression models: pa...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
Summary. Motivated by an analysis of a real data set in ecology, we consider a class of partially no...
Nonparametric and semiparametric regression models are useful statistical regression models to disco...
In this article, we propose to estimate the regression parameters in a semiparametric generalized li...
We consider the partially linear model relating a response Y to predictors (X,T) with mean function ...
We consider the semiparametric regressionXtβ+φ(Z) where β and φ(·) are unknown slope coefficient vec...