Matrix tricks for linear statistical models : our personal top twenty / Simo Puntanen, George P.H. Styan, Jarkko Isotalo.
Material type: TextLanguage: English Publication details: Berlin ; New York : Springer, c2011. Description: xvii, 486 p. : ill. ; 24 cmISBN: 9783642104725 (hbk.); 364210472X (hbk.)Subject(s): Linear models (Statistics) | Matrix analytic methodsDDC classification: 519.535 LOC classification: QA276 | .P83 2011Online resources: WorldCat details | E-book FulltextItem type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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E-Book | Dr. S. R. Lasker Library, EWU E-book | Non-fiction | 519.535 PUM 2011 (Browse shelf(Opens below)) | Not For Loan | ||||
Text | Dr. S. R. Lasker Library, EWU Reserve Section | Non-fiction | 519.535 PUM 2011 (Browse shelf(Opens below)) | C-1 | Not For Loan | 26499 |
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519.535 MCI 2007 Introduction to statistics for biology / | 519.535 NEI 2006 An introduction to copulas / | 519.535 PES 2010 A SAS/IML companion for linear models / | 519.535 PUM 2011 Matrix tricks for linear statistical models : | 519.535 REL 2008 Linear models in statistics / | 519.535 REM 2012 Methods of multivariate analysis / | 519.536 ANR 2010 Regression with linear predictors / |
Includes bibliographical references (p. 439-468) and indexes.
Easy column space tricks --
Easy projector tricks --
Easy correlation tricks --
Generalized inverses in a nutshell --
Rank of the partitioned matrix and the matrix product --
Rank cancellation rule --
Sum of orthogonal projectors --
A decomposition of the orthogonal projector --
Minimizing cov(y --
FX) --
BLUE --
General solution to AYB=C --
Invariance with respect to the choice of generalized inverse --
Block-diagonalization and the Schur complement --
Nonnegative definiteness of a partitioned matrix --
The matrix Ṁ --
Disjointness of column spaces --
Full rank decomposition --
Eigenvalue decomposition --
Singular value decomposition --
The Cauchy-Schwarz inequality --
Notation --
List of figures and philatelic items.
Summary:
In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear Read more...
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