Comparison of some biased estimation methods (including ordinary subset regression) in the linear model
- Sidik, S. M.
- Apr 1, 1975.
- Physical Description:
- 1 electronic document
- Restrictions on Access:
- Unclassified, Unlimited, Publicly available.
- Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.
- NASA Technical Reports Server (NTRS) Collection.
- Document ID: 19750013001., Accession ID: 75N21073., E-8180., and NASA-TN-D-7932.
- No Copyright.
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