The Lagrange multiplier test, often adopted to detect heteroscedasticity, suffers from severe size distortion and has low power. An existing robust test based on a forward search algorithm has shown better performance than many existing robust methods. Nevertheless, such a forward robust test relies on con¯dence bands based on the Student's-t distribution which hold only approximately. The robust forward weighted Lagrange multiplier test can be improved through extensive simulation of forward search con¯dence bands, which are set up under the hypothesis of no outlier in the data
The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust r...
A simple test for heteroscedastic disturbances in a linear regression model is developed using the f...
Lagrange multiplier (LM) test statistics are derived for testing a linear moving average model again...
Statistical tests routinely adopted for detecting nonlinear components in time series rely on the au...
Statistical tests routinely adopted for detecting nonlinear components in time series rely on the au...
Statistical tests routinely adopted for detecting nonlinear components in time series rely on the au...
Statistical tests routinely adopted for detecting nonlinear components in time series rely on the au...
A simple robust method is provided to test the goodness of fit for the extreme value distribution (Ty...
Robustness of Linear Mixed Models (LMM) with random effects is investigated with the forward search ...
In this paper we show how the forward search, free from masking and swamping problems, can detect ma...
The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust r...
The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust r...
none4siThis article studies the Type I error, false positive rates, and power of four versions of th...
This article studies the Type I error, false positive rates, and power of four versions of the Lagra...
This article studies the Type I error, false positive rates, and power of four versions of the Lagra...
The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust r...
A simple test for heteroscedastic disturbances in a linear regression model is developed using the f...
Lagrange multiplier (LM) test statistics are derived for testing a linear moving average model again...
Statistical tests routinely adopted for detecting nonlinear components in time series rely on the au...
Statistical tests routinely adopted for detecting nonlinear components in time series rely on the au...
Statistical tests routinely adopted for detecting nonlinear components in time series rely on the au...
Statistical tests routinely adopted for detecting nonlinear components in time series rely on the au...
A simple robust method is provided to test the goodness of fit for the extreme value distribution (Ty...
Robustness of Linear Mixed Models (LMM) with random effects is investigated with the forward search ...
In this paper we show how the forward search, free from masking and swamping problems, can detect ma...
The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust r...
The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust r...
none4siThis article studies the Type I error, false positive rates, and power of four versions of th...
This article studies the Type I error, false positive rates, and power of four versions of the Lagra...
This article studies the Type I error, false positive rates, and power of four versions of the Lagra...
The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust r...
A simple test for heteroscedastic disturbances in a linear regression model is developed using the f...
Lagrange multiplier (LM) test statistics are derived for testing a linear moving average model again...