Approximate maximum likelihood estimation in linear regression
β Scribed by Michael A. Magdalinos
- Publisher
- Springer Japan
- Year
- 1993
- Tongue
- English
- Weight
- 724 KB
- Volume
- 45
- Category
- Article
- ISSN
- 0020-3157
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## Abstract In previous work by Stoica and Viberg the reducedβrank regression problem is solved in a maximum likelihood sense. The present paper proposes an alternative numerical procedure. The solution is written in terms of the principal angles between subspaces spanned by the data matrices. It i
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,