Format:
1 Online-Ressource (xiii, 199 pages)
,
digital, PDF file(s)
ISBN:
9780511546822
Series Statement:
Cambridge series on statistical and probabilistic mathematics 19
Content:
This book is about the coordinate-free, or geometric, approach to the theory of linear models; more precisely, Model I ANOVA and linear regression models with non-random predictors in a finite-dimensional setting. This approach is more insightful, more elegant, more direct, and simpler than the more common matrix approach to linear regression, analysis of variance, and analysis of covariance models in statistics. The book discusses the intuition behind and optimal properties of various methods of estimating and testing hypotheses about unknown parameters in the models. Topics covered range from linear algebra, such as inner product spaces, orthogonal projections, book orthogonal spaces, Tjur experimental designs, basic distribution theory, the geometric version of the Gauss-Markov theorem, optimal and non-optimal properties of Gauss-Markov, Bayes, and shrinkage estimators under assumption of normality, the optimal properties of F-test, and the analysis of covariance and missing observations
Content:
Topics in linear algebra -- Random vectors -- Gauss-Markov estimation -- Normal theory: estimation -- Normal theory: testing -- Analysis of covariance -- Missing observations
Note:
Title from publisher's bibliographic system (viewed on 05 Oct 2015)
Additional Edition:
ISBN 9780521868426
Additional Edition:
Print version ISBN 9780521868426
Language:
English
Subjects:
Economics
Keywords:
Lineares Regressionsmodell
;
Kovarianz
;
Varianzanalyse
DOI:
10.1017/CBO9780511546822
URL:
Volltext
(lizenzpflichtig)
Bookmarklink
