On admissible estimation for parametric functions in linear models
Copyright policy )On admissible estimation for parametric functions in linear models
Consider a linear model E y\(=\theta \in K\), cov \(y\in V\), and let (a,y) be a linear estimator of some parametric function (p,\(\theta)\). Under the squared error loss function \(((a,y)-(p,\theta))^ 2\) the authors investigate whether or not (a,y) is admissible for (p,\(\theta)\). They also discuss when a linear mapping C: \(K\to K\) has the property that (a,Cy) is an admissible estimator of (Ey,a) for all \(a\in K\). The question of replacing inadmissible estimators by admissible ones is also discussed.
linear model, Statistical decision theory, Linear inference, regression, Linear regression; mixed models, linear mapping, admissible estimator, linear estimator, squared error loss, Admissibility in statistical decision theory, inadmissible estimators
linear model, Statistical decision theory, Linear inference, regression, Linear regression; mixed models, linear mapping, admissible estimator, linear estimator, squared error loss, Admissibility in statistical decision theory, inadmissible estimators
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