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Herr, D.G. (1980) On the History of the Use of Geometry in the General Linear Model. American Statistician, 34, 43-47.
https://doi.org/10.1080/00031305.1980.10482710

has been cited by the following article:

• TITLE: A Geometric Approach to Conditioning and the Search for Minimum Variance Unbiased Estimators

  AUTHORS: James E. Marengo, David L. Farnsworth

  KEYWORDS: Conditional Variance Formula, Conditioning, Geometric Representation, Minimum Variance Estimator, Rao-Blackwell Theorem, Sufficient Statistic, Unbiased Estimator

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.3, June 25, 2021

  ABSTRACT: Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that this technique resides in the structure of an inner product space. The technique uses conditioning of an unbiased estimator on a sufficient statistic. This procedure is founded upon the conditional variance formula, which leads to an inner product space and a geometric interpretation. The example clearly illustrates the dependence on the sampling methodology. These advantages show the power and centrality of this process.