We discuss a new method of estimation of parameters in semiparametric and nonparametric models. The method is based on U-statistics constructed from quadratic influence functions. The latter extend ordinary linear influence functions of the parameter of interest as defined in semiparametric theory, and represent second order derivatives of this parameter. For parameters for which the matching cannot be perfect the method leads to a bias-variance trade-off, and results in estimators that converge at a slower than
We study the identification and estimation of semiparametric models with mismeasured endogenous regr...
Robust statistics allows the distribution of the observations to be any member of a suitable neighbo...
Currently, the high-precision estimation of nonlinear parameters such as Gini in-dices, low-income p...
There are many economic parameters that depend on nonparametric first steps. Examples include games,...
Often semiparametric estimators are asymptotically equivalent to a sample average. The object being ...
Deviations from the center within a robust neighborhood may naturally be considered an infinite dime...
This dissertation studied two main topics: inference methods for directionally differentiable functi...
We construct minimum variance unbiased estimators of von Mises functionals in estimation problems wh...
This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized es...
In this paper, I propose a set of parameters designed to identify the slope of structural relationsh...
In this paper, we focus on the variable selection for semiparametric varying coefficient partially l...
This article introduces semiparametric methods for the estimation of simultaneous equation microe-co...
<p>Despite the risk of misspecification they are tied to, parametric models continue to be used in s...
Multivariate linear models with ellipsoidal restrictions are introduced for the modelling of semipar...
A method for estimating the parameters of the Rasch model is examined. The unknown quantities in thi...
We study the identification and estimation of semiparametric models with mismeasured endogenous regr...
Robust statistics allows the distribution of the observations to be any member of a suitable neighbo...
Currently, the high-precision estimation of nonlinear parameters such as Gini in-dices, low-income p...
There are many economic parameters that depend on nonparametric first steps. Examples include games,...
Often semiparametric estimators are asymptotically equivalent to a sample average. The object being ...
Deviations from the center within a robust neighborhood may naturally be considered an infinite dime...
This dissertation studied two main topics: inference methods for directionally differentiable functi...
We construct minimum variance unbiased estimators of von Mises functionals in estimation problems wh...
This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized es...
In this paper, I propose a set of parameters designed to identify the slope of structural relationsh...
In this paper, we focus on the variable selection for semiparametric varying coefficient partially l...
This article introduces semiparametric methods for the estimation of simultaneous equation microe-co...
<p>Despite the risk of misspecification they are tied to, parametric models continue to be used in s...
Multivariate linear models with ellipsoidal restrictions are introduced for the modelling of semipar...
A method for estimating the parameters of the Rasch model is examined. The unknown quantities in thi...
We study the identification and estimation of semiparametric models with mismeasured endogenous regr...
Robust statistics allows the distribution of the observations to be any member of a suitable neighbo...
Currently, the high-precision estimation of nonlinear parameters such as Gini in-dices, low-income p...