Add to ORKGNonparametric regression is the model-based sampler's method of choice when there is serious doubt about the suitability of a linear or other simple parametric model for the survey data at hand. It supersedes the need for use of design weights and standard design-based weights. Recognition of this is especially helpful in confronting problems in sampling situations where design weights are missing or questionable. One example is the case where we have data from two (or more) samples from a given population. We discuss this case
In this paper we propose a new nonparametric regression technique. Our proposal has common ground wi...
When sample survey data with complex design (stratification, clustering, unequal selection or inclus...
Given n iid observation i(X,,Y):i=1,2,...,n) with unknown re ssion function m(.) = E(Y,/X,=.). It al...
"The literature offers two distinct reasons for incorporating sample weights into the estimation of ...
This paper studies nonparametric estimation of the regression function with surrogate outcome data u...
The efficient use of auxiliary information to improve the precision of estimation of population quan...
The literature offers two distinct reasons for incorporating sample weights into the estimation of l...
Nonparametric regression provides an important tool towards exploring the relationship between a dep...
Several questions are raised concerning differences between traditional metric multiple regression,...
Abstract. Nonparametric techniques have only recently been employed in the es-timation procedure of ...
Finite-sample properties of nonparametric regression for binary dependent variables are an-alyzed. N...
Nonparametric statistics provide a scientific methodology for cases where customary statistics are n...
For survey samples with unequal probabilities of inclusion, the Horvitz-Thompson (HT) estimator and ...
<p>In this article, a new two-step calibration technique of design weights is proposed. In the first...
This paper proposes a nonparametric bias-reduction regression estimator which can accommodate two em...
In this paper we propose a new nonparametric regression technique. Our proposal has common ground wi...
When sample survey data with complex design (stratification, clustering, unequal selection or inclus...
Given n iid observation i(X,,Y):i=1,2,...,n) with unknown re ssion function m(.) = E(Y,/X,=.). It al...
"The literature offers two distinct reasons for incorporating sample weights into the estimation of ...
This paper studies nonparametric estimation of the regression function with surrogate outcome data u...
The efficient use of auxiliary information to improve the precision of estimation of population quan...
The literature offers two distinct reasons for incorporating sample weights into the estimation of l...
Nonparametric regression provides an important tool towards exploring the relationship between a dep...
Several questions are raised concerning differences between traditional metric multiple regression,...
Abstract. Nonparametric techniques have only recently been employed in the es-timation procedure of ...
Finite-sample properties of nonparametric regression for binary dependent variables are an-alyzed. N...
Nonparametric statistics provide a scientific methodology for cases where customary statistics are n...
For survey samples with unequal probabilities of inclusion, the Horvitz-Thompson (HT) estimator and ...
<p>In this article, a new two-step calibration technique of design weights is proposed. In the first...
This paper proposes a nonparametric bias-reduction regression estimator which can accommodate two em...
In this paper we propose a new nonparametric regression technique. Our proposal has common ground wi...
When sample survey data with complex design (stratification, clustering, unequal selection or inclus...
Given n iid observation i(X,,Y):i=1,2,...,n) with unknown re ssion function m(.) = E(Y,/X,=.). It al...