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- W2110090596 abstract "The usual concept of robustness is called criterion or non-adaptive robustness to distinguish it from inference or adaptive robustness. The former term is appled to describe relative insensitivity to changes in the parent distribution, while the latter specifically implies dependence on and hence adaptation to changes in the parent distribution. It is argued that knowledge of, and sensitivity to the parent distribution is an important aspect of inference, and thus the latter concept of robustness is more relevant than the former. This focuses attention on adaptive procedures that use most of the sample information, that is, are efficient. Maximum likelihood has been criticized as depending critically on knowledge of the exact parent distribution, and hence of lacking criterion or non-adaptive robustness. This might have been justified when computational parameter to allow for uncertainly of shape. then the method of maximim likelihood is hsown to possess the more important requirement of being adaptive and efficent, capable of assessing the more relevant creiterion of inference or adaptive robustness." @default.
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- W2110090596 date "1982-01-01" @default.
- W2110090596 modified "2023-09-25" @default.
- W2110090596 title "Robustness and maximum likelihood estimation" @default.
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- W2110090596 doi "https://doi.org/10.1080/03610918208828403" @default.
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