The aim of this note is to provide a general framework for the analysis of the robustness properties of a broad class of two-stage models. We derive the influence function, the change-of-variance function, and the asymptotic variance of a general two-stage Mestimator, and provide their interpretations. We illustrate our results in the case of the two-stage maximum likelihood estimator and the two-stage least squares estimator
AbstractOne of the advantages for the varying-coefficient model is to allow the coefficients to vary...
In this paper we study how the Huber estimator can be adapted to the presence of endogeneity in a tw...
An S-estimator is defined for the one-way random effects model, analogous to an S-estimator in the m...
This article discusses estimates of variance for two-stage models. We present the sandwich estimate ...
This article discusses estimates of variance for two-stage models. We present the sandwich estimate ...
AbstractIn this paper we derive the change-of-variance function of M-estimators of scale under gener...
In this paper we derive the change-of-variance function of M-estimators of scale under general conta...
The Two-Sample Two-Stage Least Squares (TS2SLS) data combination estimator is a popular estimator fo...
The problem of non-random sample selectivity often occurs in practice in many fields. The classical ...
AbstractThe Two-Sample Two-Stage Least Squares (TS2SLS) data combination estimator is a popular esti...
A property of estimators called stability is investigated in this paper. The stability of an estimat...
This thesis is concerned with the finite sample properties of some of the most widely used two-stage...
A property of estimators called stability is investigated in this paper. The stability of an estimat...
The paper discusses the effect of model deviations such as data contamination on the maximum likelih...
We first review briefly some basic approaches to robust inference and discuss the role and the place...
AbstractOne of the advantages for the varying-coefficient model is to allow the coefficients to vary...
In this paper we study how the Huber estimator can be adapted to the presence of endogeneity in a tw...
An S-estimator is defined for the one-way random effects model, analogous to an S-estimator in the m...
This article discusses estimates of variance for two-stage models. We present the sandwich estimate ...
This article discusses estimates of variance for two-stage models. We present the sandwich estimate ...
AbstractIn this paper we derive the change-of-variance function of M-estimators of scale under gener...
In this paper we derive the change-of-variance function of M-estimators of scale under general conta...
The Two-Sample Two-Stage Least Squares (TS2SLS) data combination estimator is a popular estimator fo...
The problem of non-random sample selectivity often occurs in practice in many fields. The classical ...
AbstractThe Two-Sample Two-Stage Least Squares (TS2SLS) data combination estimator is a popular esti...
A property of estimators called stability is investigated in this paper. The stability of an estimat...
This thesis is concerned with the finite sample properties of some of the most widely used two-stage...
A property of estimators called stability is investigated in this paper. The stability of an estimat...
The paper discusses the effect of model deviations such as data contamination on the maximum likelih...
We first review briefly some basic approaches to robust inference and discuss the role and the place...
AbstractOne of the advantages for the varying-coefficient model is to allow the coefficients to vary...
In this paper we study how the Huber estimator can be adapted to the presence of endogeneity in a tw...
An S-estimator is defined for the one-way random effects model, analogous to an S-estimator in the m...