Add to ORKGThere is a vast literature on robust estimators, but in some situations it is still not easy to make inferences, such as confidence regions and hypothesis testing. This is mainly due to the following facts. On one hand, in most situations, it is difficult to derive the exact distribution of the estimator. On the other one, even if its asymptotic behaviour is known, in many cases, the convergence to the limiting distribution may be rather slow, so bootstrap methods are preferable since they often give better small sample results. However, resampling methods have several disadvantages including the propagation of anomalous data all along the new samples. In this paper, we discuss the problems arising in the bootstrap when outlying observation...
The classical bootstrap method should be used with caution in binary logistic regression model since...
Robust estimators of the seemingly unrelated regression model are considered. First, S-estimators ar...
Identification and assessment of outliers have a key role in Ordinary Least Squares (OLS) regression...
• There is a vast literature on robust estimators, but in some situations it is still not easy to ma...
There is a vast literature on robust estimators, but in some situations it is still not easy to make...
In this paper we review recent developments on a bootstrap method for robust estimators which is com...
Problem statement: Bootstrap approach had introduced new advancement in modeling and model evaluatio...
The applications of bootstrap methods in regression analysis have drawn much attention to the statis...
Seemingly unrelated regression models generalize ordinary linear regression models by considering mu...
We study the problem of performing statistical inference based on robust estimates when the distrib...
We study the problem of performing statistical inference based on robust esti-mates when the distrib...
The aim of this thesis is to analyze the properties of robust estimates and to compare these estimat...
This paper investigates the use of robust wild bootstrap techniques on regression model as an estima...
Bootstrap approximations to the sampling distribution can be seen as generalized statistics taking v...
This paper studies robustness of bootstrap inference methods for instrumental variable (IV) regressi...
The classical bootstrap method should be used with caution in binary logistic regression model since...
Robust estimators of the seemingly unrelated regression model are considered. First, S-estimators ar...
Identification and assessment of outliers have a key role in Ordinary Least Squares (OLS) regression...
• There is a vast literature on robust estimators, but in some situations it is still not easy to ma...
There is a vast literature on robust estimators, but in some situations it is still not easy to make...
In this paper we review recent developments on a bootstrap method for robust estimators which is com...
Problem statement: Bootstrap approach had introduced new advancement in modeling and model evaluatio...
The applications of bootstrap methods in regression analysis have drawn much attention to the statis...
Seemingly unrelated regression models generalize ordinary linear regression models by considering mu...
We study the problem of performing statistical inference based on robust estimates when the distrib...
We study the problem of performing statistical inference based on robust esti-mates when the distrib...
The aim of this thesis is to analyze the properties of robust estimates and to compare these estimat...
This paper investigates the use of robust wild bootstrap techniques on regression model as an estima...
Bootstrap approximations to the sampling distribution can be seen as generalized statistics taking v...
This paper studies robustness of bootstrap inference methods for instrumental variable (IV) regressi...
The classical bootstrap method should be used with caution in binary logistic regression model since...
Robust estimators of the seemingly unrelated regression model are considered. First, S-estimators ar...
Identification and assessment of outliers have a key role in Ordinary Least Squares (OLS) regression...