Results 251 to 260 of about 863,157 (269)
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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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Efficient M-estimators with auxiliary information [PDF]
Journal of Statistical Planning and Inference, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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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1995
This paper is concerned with two problems facing M- estimators. M-estimators bound the influence of large residuals, or more generally, large deviations from the mean. In doing so, however, they can become inconsistent, in particular in the case of non-Normal Generalized Linear Models (GLMs), thus leading to biased estimates.
Robert Gilchrist, George Portides
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This paper is concerned with two problems facing M- estimators. M-estimators bound the influence of large residuals, or more generally, large deviations from the mean. In doing so, however, they can become inconsistent, in particular in the case of non-Normal Generalized Linear Models (GLMs), thus leading to biased estimates.
Robert Gilchrist, George Portides
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M-Estimation (Estimating Equations)
2012In Chapter 1 we made the distinction between the parts of a fully specified statistical model. The primary part is the part that is most important for answering the underlying scientific questions. The secondary part consists of all the remaining details of the model.
Denni D Boos, L A Stefanski
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A remark on approximate M-estimators
Statistics & Probability Letters, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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1985
In this paper we introduce a new class of estimators, ridge type M-estimators, designed for analyzing linear regression models when regressor variables are multicollinear and residual distributions display long tails. The estimators are defined as weighted maximum likelihood type (M-) estimators when additional information about the parameters is given.
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In this paper we introduce a new class of estimators, ridge type M-estimators, designed for analyzing linear regression models when regressor variables are multicollinear and residual distributions display long tails. The estimators are defined as weighted maximum likelihood type (M-) estimators when additional information about the parameters is given.
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