Univariate and Multivariate General Linear Models
Theory and Applications with SAS, Second Edition
By Kevin Kim, Neil Timm
Series Editor: N. Balakrishnan
Published October 11th 2006 by Chapman and Hall/CRC – 549 pages
Published October 11th 2006 by Chapman and Hall/CRC – 549 pages
Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences.
With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis, and multivariate GLMs that cover multivariate regression analysis, MANOVA, MANCOVA, and repeated measurement data analyses. The book also analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models.
New to the Second Edition
A practical introduction to GLMs, Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework.
"The attached CD contains well-illustrated examples from various disciplines using SAS formatted data and SAS code adapted from over 200 pre-written SAS procedures."
– Kassim S. Mwitondi, Sheffield Hallam University, in Journal of Applied Statistics, June 2008
PREFACE
OVERVIEW OF THE GENERAL LINEAR MODEL
Introduction
General Linear Model
Restricted General Linear Model
Multivariate Normal Distribution
Elementary Properties of Normal Random Variables
Hypothesis Testing
Generating Multivariate Normal Data
Assessing Univariate Normality
Assessing Multivariate Normality with Chi-Square Plots
Using SAS INSIGHT
Three-Dimensional Plots
UNRESTRICTED GENERAL LINEAR MODELS
Introduction
Linear Models without Restrictions
Hypothesis Testing
Simultaneous Inference
Multiple Linear Regression
Linear Mixed Models
One-Way Analysis of Variance
Multiple Linear Regression: Calibration
Two-Way Nested Designs
Intraclass Covariance Models
RESTRICTED GENERAL LINEAR MODELS
Introduction
Estimation and Hypothesis Testing
Two-Way Factorial Design without Interaction
Latin Square Designs
Repeated Measures Designs
Analysis of Covariance
WEIGHTED GENERAL LINEAR MODELS
Introduction
Estimation and Hypothesis Testing
OLSE versus FGLS
General Linear Mixed Model Continued
Maximum Likelihood Estimation and Fisher's Information Matrix
WLSE for data Data Heteroscedasticity
WLSE for Correlated Errors
FGLS for Categorical Data
MULTIVARIATE GENERAL LINEAR MODELS
Introduction
Developing the Model
Estimation Theory and Hypothesis Testing
Multivariate Regression
Classical and Normal Multivariate Linear Regression Models
Jointly Multivariate Normal Regression Model
Multivariate Mixed Models and the Analysis of Repeated Measurements
Extended Linear Hypotheses
Multivariate Regression: Calibration and Prediction
Multivariate Regression: Influential Observations
Nonorthogonal MANOVA Designs
MANCOVA Designs
Stepdown Analysis
Repeated Measures Analysis
Extended Linear Hypotheses
DOUBLY MULTIVARIATE LINEAR MODEL
Introduction
Classical Model Development
Responsewise Model Development
The Multivariate Mixed Model
Double Multivariate and Mixed Models
RESTRICTED MGLM AND GROWTH CURVE MODEL
Introduction
Restricted Multivariate General Linear Model
The GMANOVA Model
Canonical Form of the GMANOVA Model
Restricted Nonorthogonal Three-Factor Factorial MANOVA
Restricted Intraclass Covariance Design
Growth Curve Analysis
Multiple Response Growth Curves
Single Growth Curve
SUR MODEL AND RESTRICTED GMANOVA MODEL
Introduction
MANOVA-GMANOVA Model
Tests of Fit
Sum of Profiles and CGMANOVA Models
SUR Model
Restricted GMANOVA Model
GMANOVA-SUR: One Population
GMANOVA-SUR: Several Populations
SUR Model
Two-Period Crossover Design with Changing Covariates
Repeated Measurements with Changing Covariates
MANOVA-GMANOVA Model
CGMANOVA Model
SIMULTANEOUS INFERENCE USING FINITE INTERSECTION TESTS
Introduction
Finite Intersection Tests
Finite Intersection Tests of Univariate Means
Finite Intersection Tests for Linear Models
Comparison of Some Tests of Univariate Means with the FIT Procedure
Analysis of Means Analysis
Simultaneous Test Procedures for Mean Vectors
Finite Intersection Test of Mean Vectors
Finite Intersection Test of Mean Vectors with Covariates
Summary
Univariate: One-Way ANOVA
Multivariate: One-Way MANOVA
Multivariate: One-Way MANCOVA
COMPUTING POWER FOR UNIVARIATE AND MULTIVARIATE GLM
Introduction
Power for Univariate GLMs
Estimating Power, Sample Size, and Effect Size for the GLM
Power and Sample Size Based on Interval Estimation
Calculating Power and Sample Size for Some Mixed Models
Power for Multivariate GLMs
Power and Effect Size Analysis for Univariate GLMs
Power and Sample Size Based on Interval Estimation
Power Analysis for Multivariate GLMs
TWO-LEVEL HIERARCHICAL LINEAR MODELS
Introduction
Two-Level Hierarchical Linear Models
Random Coefficient Model: One Population
Random Coefficient Model: Several Populations
Mixed Model Repeated Measures
Mixed Model Repeated Measures with Changing Covariates
Application: Two-Level Hierarchical Linear Models
INCOMPLETE REPEATED MEASUREMENT DATA
Introduction
Missing Mechanisms
FGLS Procedure
ML Procedure
Imputations
Repeated Measures Analysis
Repeated Measures with Changing Covariates
Random Coefficient Model
Growth Curve Analysis
STRUCTURAL EQUATION MODELING
Introduction
Model Notation
Estimation
Model Fit in Practice
Model Modification
Summary
Path Analysis
Confirmatory Factor Analysis
General SEM
REFERENCES
AUTHOR INDEX
SUBJECT INDEX
Name: Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition (Hardback) – Chapman and Hall/CRC
Description: By Kevin Kim, Neil TimmSeries Editor: N. Balakrishnan. Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and...
Categories: Regression Analysis and Multivariate Statistics, Statistical Theory & Methods, Quantitative Methods, Statistics & Computing