An intuitive geometric approach to the Gauss Markov theorem

dc.creator	Pereira, Leandro da Silva
dc.creator	Chaves, Lucas Monteiro
dc.creator	Souza, Devanil Jaques de
dc.date.accessioned	2018-07-27T12:14:54Z
dc.date.available	2018-07-27T12:14:54Z
dc.date.issued	2017
dc.description.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.	pt_BR
dc.identifier.citation	PEREIRA, L. da S.; CHAVES, L. M.; SOUZA, D. J. de. An intuitive geometric approach to the Gauss Markov theorem. The American Statistician, [S. l.], v. 71, n. 1, p. 67-70, 2017.	pt_BR
dc.identifier.uri	https://repositorio.ufla.br/handle/1/29792
dc.identifier.uri	https://amstat.tandfonline.com/doi/abs/10.1080/00031305.2016.1209127#.W09lFtVKgdU	pt_BR
dc.language	en_US	pt_BR
dc.publisher	American Statistical Association	pt_BR
dc.rights	openAccess	pt_BR
dc.source	The American Statistician	pt_BR
dc.subject	Dispersion cloud of points	pt_BR
dc.subject	Gauss–Markov estimator	pt_BR
dc.subject	Orthogonal projection	pt_BR
dc.subject	Nuvem de dispersão de pontos	pt_BR
dc.subject	Estimador de Gauss-Markov	pt_BR
dc.subject	Projeção ortogonal	pt_BR
dc.title	An intuitive geometric approach to the Gauss Markov theorem	pt_BR
dc.type	Artigo	pt_BR

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