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Optimal Data Augmentation for the Estimation of a Linear Parametric Function in Linear Models

Dulal Kumar Bhaumik and Thomas Mathew
Sankhyā: The Indian Journal of Statistics, Series B (1960-2002)
Vol. 63, No. 1 (Apr., 2001), pp. 10-26
Stable URL: http://www.jstor.org/stable/25042377
Page Count: 17
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Optimal Data Augmentation for the Estimation of a Linear Parametric Function in Linear Models
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Abstract

In the setup of a linear regression model, the problem of augmenting a given set of observations is investigated, when the inference problem is the estimation of a linear parametric function of the mean vector. For this problem, the optimal selection of additional observations is studied. The optimal design matrix is constructed following the A, D and E-optimality criteria. The results are illustrated with an example.

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