Titlebook: Robust Planning and Analysis of Experiments; Christine H. Müller Book 1997 Springer-Verlag New York, Inc. 1997 Outlook.Smooth function.Vol

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Book 1997e has been very little overlap between these fields. In robust statistics, robust alternatives to the nonrobust least squares estimator have been developed, while in experimental design, designs for the efficient use of the least square estimator have been developed. This volume is the first to link

Culpable

0930-0325 now, there has been very little overlap between these fields. In robust statistics, robust alternatives to the nonrobust least squares estimator have been developed, while in experimental design, designs for the efficient use of the least square estimator have been developed. This volume is the fir

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植物群

High Robustness and High Efficiency of Testsl robust testing are derived. Thereby, in both sections we assume that the ideal model is a homoscedastic linear model with normally distributed errors, i.e. the error . at . is distributed according to the normal distribution n(0, σ.) with mean 0 and variance . for all .. In particular, we have ..

細胞

Efficiency Concepts for Outlier-Free Observationsion is ideal, i.e. where no outliers or other deviations appear. At first in Section 2.1 the ideal distribution of the errors is given. Then in Section 2.2 the efficiency concepts are given for estimating or testing a linear aspect in a linear model. Efficiency concepts for estimating a nonlinear as

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Robustness Measures: Bias and Breakdown Pointsof the ideal distribution .. NOW, in Section 4.1 we will derive robustness properties of estimators by regarding the behaviour of their corresponding functionals in neighbourhoods which are not infinitesimal small. This behaviour is important for situations in which the amount of outliers is not dec

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Asymptotic Robustness for Shrinking Contaminationd on shrinking neighbourhoods. In particular, the concepts coincide for estimators given by Frechet differentiable functionals as is shown in Section 5.1. But Section 5.1 also shows that for robustness concepts based on shrinking neighbourhoods also the larger class of asymptotically linear estimato