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M‐estimation in the presence of unequal scale
Statistica Neerlandica, 1980Abstract The class of weighted M‐estimators is defined. The ratio of the asymptotic variance of the weighted estimator to the asymptotic variance of the optimally weighted estimator is defined as the inefficiency. A Kantorovich inequality is proved, its implications are investigated for the misweighted mean and misweighted median, and the results are ...
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M-Estimation in Cross-Over Trials
Biometrics, 1994A robust procedure, combined M-estimation, is proposed for analyzing cross-over data with possible within- and between-subject outliers. The mean squared error properties of these combined M-estimates for direct treatment effect contrasts and carryover treatment effect contrasts are examined through simulation studies.
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1996
We consider a linear regression model $$y = X\beta + \varepsilon $$ where y is a response variable, X is an n×p design matrix of rank p, and ∈ is a vector with i.i.d. random variables.
Håkan Ekblom, Hans Bruun Nielsen
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We consider a linear regression model $$y = X\beta + \varepsilon $$ where y is a response variable, X is an n×p design matrix of rank p, and ∈ is a vector with i.i.d. random variables.
Håkan Ekblom, Hans Bruun Nielsen
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A Mirror Image Invariance for M-Estimators
Econometrica, 1995Much research into the performance of econometric estimators utilizes Monte Carlo experiments, either as a primary tool or as a check on the accuracy of first or higher order asymptotic approximations. It is important to design efficient experiments and to extract all the information they provide.
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1986
Robust partitioning algorithms for isotonic regression are shown to have anomalous behavior.
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Robust partitioning algorithms for isotonic regression are shown to have anomalous behavior.
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1983
The type M estimators, also called M estimators, are generalizations of the usual maximum likelihood estimates. ϑ is classically the parameter value maximizing the likelihood function, i. e. we have in obvious notation $$ L = \Pi f({x_i}|\vartheta ) = \max {\rm{for }}\vartheta $$ or equivalently $$ - \ln {\rm{ }}L{\rm{ = - }}\sum {\rm{ ln ...
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The type M estimators, also called M estimators, are generalizations of the usual maximum likelihood estimates. ϑ is classically the parameter value maximizing the likelihood function, i. e. we have in obvious notation $$ L = \Pi f({x_i}|\vartheta ) = \max {\rm{for }}\vartheta $$ or equivalently $$ - \ln {\rm{ }}L{\rm{ = - }}\sum {\rm{ ln ...
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