The least trimmed squares estimator and the minimum covari- ance determinant estimator [5] are frequently used robust estimators of re- gression and of location and scatter. Consistency factors can be computed for both methods to make the estimators consistent at the normal model. However, for small data sets these factors do not make the estimator un- biased. Based on simulation studies we therefore construct formulas which allow us to compute small sample correction factors for all sample sizes and dimensions without having to carry out any new simulations. We give some examples to illustrate the effect of the correction factor.status: publishe
In a robust analysis, the minimum volume ellipsoid (MVE) estimator is very often used to estimate bo...
There is a need for appropriate methods for the analysis of very small samples of continuous repeate...
The Minimum Covariance Determinant (MCD) estimator is a highly robust procedure for estimating the c...
Abstract. The least trimmed squares estimator and the minimum covariance determinant estimator [6] a...
An approximate small sample variance estimator for fixed effects from the multivariate normal linear...
The most popular and perhaps universal estimator of location and scale in robust estimation, where o...
The minimum covariance determinant (MCD) estimators of multivariate location and scatter are robust ...
AbstractIn modern statistics the robust estimation of parameters is a central problem, i.e., an esti...
We deal with the equivariant estimation of scatter and location for p-dimensional data, giving empha...
The minimum covariance determinant (MCD) approach estimates the location and scatter matrix using th...
In modern statistics the robust estimation of parameters is a central problem, i. e., an estimation ...
In experimental science measurements are typically repeated only a few times, yielding a sample size...
The Minimum Covariance Determinant (MCD) estimator is a highly robust procedure for estimating the ...
The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of ...
Consistency is shown for the minimum covariance determinant (MCD) estimators of multivariate locatio...
In a robust analysis, the minimum volume ellipsoid (MVE) estimator is very often used to estimate bo...
There is a need for appropriate methods for the analysis of very small samples of continuous repeate...
The Minimum Covariance Determinant (MCD) estimator is a highly robust procedure for estimating the c...
Abstract. The least trimmed squares estimator and the minimum covariance determinant estimator [6] a...
An approximate small sample variance estimator for fixed effects from the multivariate normal linear...
The most popular and perhaps universal estimator of location and scale in robust estimation, where o...
The minimum covariance determinant (MCD) estimators of multivariate location and scatter are robust ...
AbstractIn modern statistics the robust estimation of parameters is a central problem, i.e., an esti...
We deal with the equivariant estimation of scatter and location for p-dimensional data, giving empha...
The minimum covariance determinant (MCD) approach estimates the location and scatter matrix using th...
In modern statistics the robust estimation of parameters is a central problem, i. e., an estimation ...
In experimental science measurements are typically repeated only a few times, yielding a sample size...
The Minimum Covariance Determinant (MCD) estimator is a highly robust procedure for estimating the ...
The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of ...
Consistency is shown for the minimum covariance determinant (MCD) estimators of multivariate locatio...
In a robust analysis, the minimum volume ellipsoid (MVE) estimator is very often used to estimate bo...
There is a need for appropriate methods for the analysis of very small samples of continuous repeate...
The Minimum Covariance Determinant (MCD) estimator is a highly robust procedure for estimating the c...