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M-Estimation for dependent random variables
Statistics & Probability Letters, 2002This paper discusses the consistency in the strong sense and essential uniqueness of M-estimation for dependent random variables. The hypotheses are based on the function defining implicitly the M-estimation as well as on its first derivative and its Hessian matrix.
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The American Statistician, 2002
Since the seminal papers by Huber in the 1960s, M-estimation methods (also known as estimating equation methods) have been increasingly important for asymptotic analysis and approximate inference. This article illustrates the breadth and generality of the M-estimation approach, thereby facilitating its use inpractice and in the classroom as a unifying ...
Stefanski L. A., Boos D. D.
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Since the seminal papers by Huber in the 1960s, M-estimation methods (also known as estimating equation methods) have been increasingly important for asymptotic analysis and approximate inference. This article illustrates the breadth and generality of the M-estimation approach, thereby facilitating its use inpractice and in the classroom as a unifying ...
Stefanski L. A., Boos D. D.
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Convergence of the optimal M-estimator over a parametric family of M-estimators
Test, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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