Results 231 to 240 of about 865,609 (245)
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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.
openaire +1 more source