Optimal Estimation in a Linear Regression Model using Incomplete Prior Information
For the estimation of regression coefficients in a linear model when incomplete prior information is available, the optimal estimators in the classes of linear heterogeneous and linear homogeneous estimators are considered. As they involve some unknowns, they are operationalized by substituting unbiased estimators for the unknown quantities. The properties of resulting feasible estimators are analyzed and the effect of operationalization is studied. A comparison of the heterogeneous and homogeneous estimation techniques is also presented.
KeywordsLinear Regression Model Risk Function Variance Covariance Matrix Optimal Estimator Multivariate Normal Distribution
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