Statistical Methods: The Geometric Approach

DOI10.1007/978-1-4612-0971-3zbMath0747.62005OpenAlexW4292494700MaRDI QIDQ3999763

David J. Saville, Graham R. Wood

Publication date: 17 September 1992

Published in: Springer Texts in Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-1-4612-0971-3

zbMATH Keywords

matrix methodsANOVAblockingexercisesexamplesanalysis of covariance

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01) Analysis of variance and covariance (ANOVA) (62J10)

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An Intuitive Geometric Approach to the Gauss Markov Theorem ⋮ The Kullback-Leibler risk of the Stein estimator and the conditional MLE ⋮ A geometric approach to finite sample and large deviation properties in two-way ANOVA with spherically distributed error vectors ⋮ Size change, shape change, and the growth space of a community ⋮ Design issues related to allocation of experimental units with known covariates into two treatment groups ⋮ Ordered Multiple Comparisons with the Best and Their Applications to Dose–Response Studies ⋮ On the geometry ofF, Wald, LR, and LM tests in linear regression models ⋮ A geometric interpretation of Mallows' \(C_p\) statistic and an alternative plot in variable selection ⋮ The Ubiquitous Angle ⋮ Binomial mixtures: Geometric estimation of the mixing distribution ⋮ Elliptical insights: understanding statistical methods through elliptical geometry

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