Titlebook: Directions in Robust Statistics and Diagnostics; Part II Werner Stahel,Sanford Weisberg Conference proceedings 1991 Springer-Verlag New Yor

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AWL

Configural Polysampling, sampling distributions. By including a heavy-tailed distribution along with the Gaussian in the collection of sampling distributions, inference procedures that are robust against heavy-tailed deviations from the Gaussian are obtained. The conditional point of view we referred to, can - and should -

SCORE

Functional

Unmasking Multivariate Outliers and Leverage Points by Means of BMDP3R,te location and covariance are considered, using the P3R and PAR programs from the widely available statistical package BMDP. Multivariate outliers are detected using robust distances based on the MVE estimators of the explanatory variables. The identification of leverage points in linear regression

enormous

血友病

血友病

Robust Testing of Functionals,ur paper, the multisided testing problem about .,.which extends the classical multiparameter hypotheses to nonparametrized measures, is studied using a locally asymptotic robust approach. Assuming any full infinitesimal neighborhoods, and i.i.d. observations, we derive a locally asymptotic maximin (

Picks-Disease

Robustness of the ,-Subset Algorithm for Regression with High Breakdown Point,es estimator. Unfortunately, its exact computation is quite difficult because the objective function may have a large number of local minima. Therefore, we have been using an approximate algorithm based on .-subsets. In this paper we prove that the algorithm shares the equivariance and good breakdow

concubine

Robust Distances: Simulations and Cutoff Values,gested to use the Minimum Volume Ellipsoid estimator, which can be computed using a resampling algorithm. In this paper the small sample behavior of the robust distances is studied by means of simulation. We obtain a correction factor yielding approximately correct coverage percentages for the corre

明智的人

Diagnostics for Regression-Arma Time Series,ype II) outlier. Commonly these outliers have been modelled as mean-shift outliers, but variance-shift outliers may also be considered. In this paper we formulate the resulting 4 outlier types (mean-shift observation outlier, variance-shift observation outlier, mean-shift innovation outlier, and var