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Casella, G., Berger, R. and Sanatana, D. (2001) Solutions Manual for Statistical Inference, Second Edition.
http://exampleproblems.com/Solutions-Casella-Berger.pdf

has been cited by the following article:

  • TITLE: Minimizing the Variance of a Weighted Average

    AUTHORS: Doron J. Shahar

    KEYWORDS: Variance, Weighted Average, Minimization

    JOURNAL NAME: Open Journal of Statistics, Vol.7 No.2, April 24, 2017

    ABSTRACT: It is common practice in science to take a weighted average of estimators of a single parameter. If the original estimators are unbiased, any weighted average will be an unbiased estimator as well. The best estimator among the weighted averages can be obtained by choosing weights that minimize the variance of the weighted average. If the variances of the individual estimators are given, the ideal weights have long been known to be the inverse of the variance. Nonetheless, I have not found a formal proof of this result in the literature. In this article, I provide three different proofs of the ideal weights.