Flexible L-estimation in the linear model
✍ Scribed by Yadolah Dodge; Jana Jurečková
- Publisher
- Elsevier Science
- Year
- 1991
- Tongue
- English
- Weight
- 638 KB
- Volume
- 12
- Category
- Article
- ISSN
- 0167-9473
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This paper addresses the problem of maximum likelihood parameter estimation in linear models a!ected by Gaussian noise, whose mean and covariance matrix are uncertain. The proposed estimate maximizes a lower bound on the worst-case (with respect to the uncertainty) likelihood of the measured sample,
We consider a linear normal model Y=X%+e with % verifying a linear restriction and the standard estimators % (unrestricted MLE) and %\* (restricted MLE). We prove that %\* is preferable to % using a new and strong criterion which implies the domination under other usual criteria; in particular it is