An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, is analyzed with the purpose of finding an estimator that is robust to outliers and structural breaks. This estimator is an example of a one-step M-estimator based on Huber's skip function. The asymptotic theory is derived in the situation where there are no outliers or structural breaks using empirical process techniques. Stationary processes, trend stationary autoregressions and unit root processes are considered
M-estimators are used as a robust replacement of the general classical estimators used in the field ...
It is argued that model selection and robust estimation should be handled jointly.Impulse indicator ...
Abstract. There are several methods for obtaining very robust estimates of regression parameters tha...
An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than obs...
An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than obs...
Preliminary version Several algorithms for indicator saturation are compared and found to have low p...
The indicator saturation approach is one of the latest methods in the literature that Can detect bo...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
Outlier detection algorithms are intimately connected with robust statistics that down-weight some o...
The Robustified Least Squares and the Impulse Indicator Saturation are iterative algorithms concerne...
Robust methods are little applied (although much studied by statisticians). We monitor very robust r...
In this thesis, we study a “heuristic approach” that are frequently used for outlier robustness anal...
This thesis is about Step-indicator Saturation, an algorithm that identifies and models location sh...
Structural change affects the estimation of economic signals, such as the growth rate or the seasona...
Regression analysis plays a vital role in many areas of science. Almost all regression analyses rely...
M-estimators are used as a robust replacement of the general classical estimators used in the field ...
It is argued that model selection and robust estimation should be handled jointly.Impulse indicator ...
Abstract. There are several methods for obtaining very robust estimates of regression parameters tha...
An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than obs...
An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than obs...
Preliminary version Several algorithms for indicator saturation are compared and found to have low p...
The indicator saturation approach is one of the latest methods in the literature that Can detect bo...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
Outlier detection algorithms are intimately connected with robust statistics that down-weight some o...
The Robustified Least Squares and the Impulse Indicator Saturation are iterative algorithms concerne...
Robust methods are little applied (although much studied by statisticians). We monitor very robust r...
In this thesis, we study a “heuristic approach” that are frequently used for outlier robustness anal...
This thesis is about Step-indicator Saturation, an algorithm that identifies and models location sh...
Structural change affects the estimation of economic signals, such as the growth rate or the seasona...
Regression analysis plays a vital role in many areas of science. Almost all regression analyses rely...
M-estimators are used as a robust replacement of the general classical estimators used in the field ...
It is argued that model selection and robust estimation should be handled jointly.Impulse indicator ...
Abstract. There are several methods for obtaining very robust estimates of regression parameters tha...
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