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The Coordinate-Free Approach to Linear Models (Cambridge Series in Statistical and Probabilistic Mathematic)

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The Coordinate-Free Approach to Linear Models (Cambridge Series in Statistical and Probabilistic Mathematic) Cover

 

Synopses & Reviews

Publisher Comments:

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 nonrandom 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 include inner product spaces, orthogonal projections, book orthogonal spaces, Tjur experimental designs, basic distribution theory, the geometric version of the Gauss-Markov theorem, optimal and nonoptimal properties of Gauss-Markov, Bayes, and shrinkage estimators under the assumption of normality, the optimal properties of F-tests, and the analysis of covariance and missing observations.

Synopsis:

The coordinate-free, or geometric, approach to the theory of linear models 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. This book treats Model I ANOVA and linear regression models with non-random predictors in a finite-dimensional setting. The author discusses the intuition behind and optimal properties of various methods of estimating and testing hypotheses about unknown parameters in the models, offering statistics graduate students and researchers a new angle on a classic subject.

Synopsis:

Treats Model I ANOVA and linear regression models with non-random predictors in a finite-dimensional setting.

About the Author

Professor Wichura has 37 years of teaching experience in the Department of Statistics at the University of Chicago. He has served as an Associate Editor for the Annals of Probability and was the Database Editor for the Current Index to Statistics from 1995 to 2000. He is the author of the PiCTeX macros (for drawing pictures in TeX) and the PiCTeX manual, and also of the TABLE macros and the TABLE manual.

Product Details

ISBN:
9780521868426
Author:
Wichura, Michael J.
Publisher:
Cambridge University Press
Editor:
Gill, R.
Editor:
Ripley, B. D.
Location:
Cambridge
Subject:
Linear Programming
Subject:
Statistics
Subject:
Regression analysis
Subject:
Linear models (statistics)
Subject:
Probability & Statistics - General
Subject:
Analysis of variance
Subject:
Mathematics | Probability and Statistics
Series:
Cambridge Series in Statistical and Probabilistic Mathematic
Publication Date:
20061031
Binding:
Hardcover
Grade Level:
Professional and scholarly
Language:
English
Illustrations:
Y
Pages:
199
Dimensions:
10.30x7.14x.69 in. 1.20 lbs.

Related Subjects

Science and Mathematics » Biology » General
Science and Mathematics » Materials Science » General
Science and Mathematics » Mathematics » Combinatorics
Science and Mathematics » Mathematics » Computer
Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

The Coordinate-Free Approach to Linear Models (Cambridge Series in Statistical and Probabilistic Mathematic) New Hardcover
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$81.75 In Stock
Product details 199 pages Cambridge University Press - English 9780521868426 Reviews:
"Synopsis" by , The coordinate-free, or geometric, approach to the theory of linear models 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. This book treats Model I ANOVA and linear regression models with non-random predictors in a finite-dimensional setting. The author discusses the intuition behind and optimal properties of various methods of estimating and testing hypotheses about unknown parameters in the models, offering statistics graduate students and researchers a new angle on a classic subject.
"Synopsis" by , Treats Model I ANOVA and linear regression models with non-random predictors in a finite-dimensional setting.
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