Linear Models The Theory and Application of Analysis of Variance
Aus der Reihe
Wiley Series in Probability and Statistics
119,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
27.08.2008
Verlag
John Wiley & SonsSeitenzahl
288 (Printausgabe)
Dateigröße
8787 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9780470377970
An insightful approach to the analysis of variance in the study of
linear models
Linear Models explores the theory of linear models and the
dynamic relationships that these models have with Analysis of
Variance (ANOVA), experimental design, and random and mixed-model
effects. This one-of-a-kind book emphasizes an approach that
clearly explains the distribution theory of linear models and
experimental design starting from basic mathematical concepts in
linear algebra.
The author begins with a presentation of the classic
fixed-effects linear model and goes on to illustrate eight common
linear models, along with the value of their use in statistics.
From this foundation, subsequent chapters introduce concepts
pertaining to the linear model, starting with vector space theory
and the theory of least-squares estimation. An outline of the
Helmert matrix is also presented, along with a thorough explanation
of how the ANOVA is created in both typical two-way and higher
layout designs, ultimately revealing the distribution theory. Other
important topics covered include:
* Vector space theory
* The theory of least squares estimation
* Gauss-Markov theorem
* Kronecker products
* Diagnostic and robust methods for linear models
* Likelihood approaches to estimation
A discussion of Bayesian theory is also included for purposes of
comparison and contrast, and numerous illustrative exercises assist
the reader with uncovering the nature of the models, using both
classic and new data sets. Requiring only a working knowledge of
basic probability and statistical inference, Linear Models is a
valuable book for courses on linear models at the
upper-undergraduate and graduate levels. It is also an excellent
reference for practitioners who use linear models to conduct
research in the fields of econometrics, psychology, sociology,
biology, and agriculture.
linear models
Linear Models explores the theory of linear models and the
dynamic relationships that these models have with Analysis of
Variance (ANOVA), experimental design, and random and mixed-model
effects. This one-of-a-kind book emphasizes an approach that
clearly explains the distribution theory of linear models and
experimental design starting from basic mathematical concepts in
linear algebra.
The author begins with a presentation of the classic
fixed-effects linear model and goes on to illustrate eight common
linear models, along with the value of their use in statistics.
From this foundation, subsequent chapters introduce concepts
pertaining to the linear model, starting with vector space theory
and the theory of least-squares estimation. An outline of the
Helmert matrix is also presented, along with a thorough explanation
of how the ANOVA is created in both typical two-way and higher
layout designs, ultimately revealing the distribution theory. Other
important topics covered include:
* Vector space theory
* The theory of least squares estimation
* Gauss-Markov theorem
* Kronecker products
* Diagnostic and robust methods for linear models
* Likelihood approaches to estimation
A discussion of Bayesian theory is also included for purposes of
comparison and contrast, and numerous illustrative exercises assist
the reader with uncovering the nature of the models, using both
classic and new data sets. Requiring only a working knowledge of
basic probability and statistical inference, Linear Models is a
valuable book for courses on linear models at the
upper-undergraduate and graduate levels. It is also an excellent
reference for practitioners who use linear models to conduct
research in the fields of econometrics, psychology, sociology,
biology, and agriculture.
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