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An Intuitive Geometric Approach to the Gauss Markov Theorem

Author

Listed:
  • Leandro da Silva Pereira
  • Lucas Monteiro Chaves
  • Devanil Jaques de Souza

Abstract

Algebraic proofs of Gauss–Markov theorem are very disappointing from an intuitive point of view. An alternative is to use geometry that emphasizes the essential statistical ideas behind the result. This article presents a truly geometrical intuitive approach to the theorem, based only in simple geometrical concepts, like linear subspaces and orthogonal projections.

Suggested Citation

  • Leandro da Silva Pereira & Lucas Monteiro Chaves & Devanil Jaques de Souza, 2017. "An Intuitive Geometric Approach to the Gauss Markov Theorem," The American Statistician, Taylor & Francis Journals, vol. 71(1), pages 67-70, January.
  • Handle: RePEc:taf:amstat:v:71:y:2017:i:1:p:67-70
    DOI: 10.1080/00031305.2016.1209127
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    References listed on IDEAS

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    1. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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