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El. knyga: Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition

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  • Formatas: 549 pages
  • Išleidimo metai: 11-Oct-2006
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-13: 9781420011364
Kitos knygos pagal šią temą:
Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second EditionDidesnis paveikslėlis
  • Formatas: 549 pages
  • Išleidimo metai: 11-Oct-2006
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-13: 9781420011364
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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

Two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure

Expanded theory of unrestricted general linear, multivariate general linear, SUR, and restricted GMANOVA models to comprise recent developments

Expanded material on missing data to include multiple imputation and the EM algorithm

Applications of MI, MIANALYZE, TRANSREG, and CALIS procedures

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.
List of Tables xiii
Preface xv
1 Overview of General Linear Model
1(24)
1.1 Introduction
1(1)
1.2 General Linear Model
1(2)
1.3 Restricted General Linear Model
3(1)
1.4 Multivariate Normal Distribution
4(4)
1.5 Elementary Properties of Normal Random Variables
8(1)
1.6 Hypothesis Testing
9(1)
1.7 Generating Multivariate Normal Data
10(1)
1.8 Assessing Univariate Normality
11(4)
1.8.1 Normally and Nonnormally Distributed Data
12(3)
1.8.2 Real Data Example
15(1)
1.9 Assessing Multivariate Normality with Chi-Square Plots
15(4)
1.9.1 Multivariate Normal Data
18(1)
1.9.2 Real Data Example
19(1)
1.10 Using SAS INSIGHT
19(4)
1.10.1 Ramus Bone Data
19(2)
1.10.2 Risk-Taking Behavior Data
21(2)
1.11 Three-Dimensional Plots
23(2)
2 Unrestricted General Linear Models
25(52)
2.1 Introduction
25(1)
2.2 Linear Models without Restrictions
25(1)
2.3 Hypothesis Testing
26(2)
2.4 Simultaneous Inference
28(2)
2.5 Multiple Linear Regression
30(19)
2.5.1 Classical and Normal Regression Models
31(11)
2.5.2 Random Classical and Jointly Normal Regression Models
42(7)
2.6 Linear Mixed Models
49(4)
2.7 One-Way Analysis of Variance
53(5)
2.7.1 Unrestricted Full Rank One-Way Design
54(2)
2.7.2 Simultaneous Inference for the One-Way Design
56(2)
2.7.3 Multiple Testing
58(1)
2.8 Multiple Linear Regression: Calibration
58(12)
2.8.1 Multiple Linear Regression: Prediction
68(2)
2.9 Two-Way Nested Designs
70(2)
2.10 Intraclass Covariance Models
72(5)
3 Restricted General Linear Models
77(32)
3.1 Introduction
77(1)
3.2 Estimation and Hypothesis Testing
77(2)
3.3 Two-Way Factorial Design without Interaction
79(8)
3.4 Latin Square Designs
87(2)
3.5 Repeated Measures Designs
89(11)
3.5.1 Univariate Mixed ANOVA Model, Full Rank Representation for a Split Plot Design
90(5)
3.5.2 Univariate Mixed Linear Model, Less Than Full Rank Representation
95(2)
3.5.3 Test for Equal Covariance Matrices and for Circularity
97(3)
3.6 Analysis of Covariance
100(9)
3.6.1 ANCOVA with One Covariate
102(2)
3.6.2 ANCOVA with Two Covariates
104(1)
3.6.3 ANCOVA Nested Designs
105(4)
4 Weighted General Linear Models
109(34)
4.1 Introduction
109(1)
4.2 Estimation and Hypothesis Testing
110(3)
4.3 OLSE versus FGLS
113(1)
4.4 General Linear Mixed Model Continued
114(5)
4.4.1 Example: Repeated Measures Design
117(1)
4.4.2 Estimating Degrees of Freedom for F Statistics in GLMMs
118(1)
4.5 Maximum Likelihood Estimation and Fisher's Information Matrix
119(2)
4.6 WLSE for Data Heteroscedasticity
121(3)
4.7 WLSE for Correlated Errors
124(3)
4.8 FGLS for Categorical Data
127(16)
4.8.1 Overview of the Categorical Data Model
127(3)
4.8.2 Marginal Homogeneity
130(2)
4.8.3 Homogeneity of Proportions
132(6)
4.8.4 Independence
138(3)
4.8.5 Univariate Mixed Linear Model, Less Than Full Rank Representation
141(2)
5 Multivariate General Linear Models
143(80)
5.1 Introduction
143(1)
5.2 Developing the Model
143(2)
5.3 Estimation Theory and Hypothesis Testing
145(7)
5.4 Multivariate Regression
152(1)
5.5 Classical and Normal Multivariate Linear Regression Models
153(10)
5.6 Jointly Multivariate Normal Regression Model
163(8)
5.7 Multivariate Mixed Models and the Analysis of Repeated Measurements
171(5)
5.8 Extended Linear Hypotheses
176(6)
5.9 Multivariate Regression: Calibration and Prediction
182(7)
5.9.1 Fixed X
182(3)
5.9.2 Random X
185(1)
5.9.3 Random X, Prediction
186(1)
5.9.4 Overview — Candidate Model
186(1)
5.9.5 Prediction and Shrinkage
187(2)
5.10 Multivariate Regression: Influential Observations
189(3)
5.10.1 Results and Interpretation
191(1)
5.11 Nonorthogonal MANOVA Designs
192(8)
5.11.1 Unweighted Analysis
197(1)
5.11.2 Weighted Analysis
198(2)
5.12 MANCOVA Designs
200(6)
5.12.1 Overall Tests
200(3)
5.12.2 Tests of Additional Information
203(1)
5.12.3 Results and Interpretation
204(2)
5.13 Stepdown Analysis
206(1)
5.14 Repeated Measures Analysis
207(9)
5.14.1 Results and Interpretation
209(7)
5.15 Extended Linear Hypotheses
216(7)
5.15.1 Results and Interpretation
219(4)
6 Doubly Multivariate Linear Model
223(20)
6.1 Introduction
223(1)
6.2 Classical Model Development
223(3)
6.3 Responsewise Model Development
226(1)
6.4 The Multivariate Mixed Model
227(4)
6.5 Double Multivariate and Mixed Models
231(12)
7 Restricted MGLM and Growth Curve Model
243(54)
7.1 Introduction
243(1)
7.2 Restricted Multivariate General Linear Model
243(4)
7.3 The GMANOVA Model
247(7)
7.4 Canonical Form of the GMANOVA Model
254(5)
7.5 Restricted Nonorthogonal Three-Factor Factorial MANOVA
259(10)
7.5.1 Results and Interpretation
269(1)
7.6 Restricted Intraclass Covariance Design
269(10)
7.6.1 Results and Interpretation
275(4)
7.7 Growth Curve Analysis
279(10)
7.7.1 Results and Interpretation
283(6)
7.8 Multiple Response Growth Curves
289(5)
7.8.1 Results and Interpretation
290(4)
7.9 Single Growth Curve
294(3)
7.9.1 Results and Interpretation
294(3)
8 SUR Model and Restricted GMANOVA Model
297(52)
8.1 Introduction
297(1)
8.2 MANOVA–GMANOVA Model
297(6)
8.3 Tests of Fit
303(2)
8.4 Sum of Profiles and CGMANOVA Models
305(2)
8.5 SUR Model
307(7)
8.6 Restricted GMANOVA Model
314(3)
8.7 GMANOVA–SUR: One Population
317(2)
8.7.1 Results and Interpretation
317(2)
8.8 GMANOVA–SUR: Several Populations
319(1)
8.8.1 Results and Interpretation
319(1)
8.9 SUR Model
319(9)
8.9.1 Results and Interpretation
323(5)
8.10 Two-Period Crossover Design with Changing Covariates
328(6)
8.10.1 Results and Interpretation
329(5)
8.11 Repeated Measurements with Changing Covariates
334(3)
8.11.1 Results and Interpretation
335(2)
8.12 MANOVA–GMANOVA Model
337(7)
8.12.1 Results and Interpretation
338(6)
8.13 CGMANOVA Model
344(5)
8.13.1 Results and Interpretation
347(2)
9 Simultaneous Inference Using Finite Intersection Tests
349(32)
9.1 Introduction
349(1)
9.2 Finite Intersection Tests
349(1)
9.3 Finite Intersection Tests of Univariate Means
350(4)
9.4 Finite Intersection Tests for Linear Models
354(1)
9.5 Comparison of Some Tests of Univariate Means with the FIT Procedure
355(3)
9.5.1 Single-Step Methods
355(2)
9.5.2 Stepdown Methods
357(1)
9.6 Analysis of Means Analysis
358(2)
9.7 Simultaneous Test Procedures for Mean Vectors
360(2)
9.8 Finite Intersection Test of Mean Vectors
362(4)
9.9 Finite Intersection Test of Mean Vectors with Covariates
366(2)
9.10 Summary
368(1)
9.11 Univariate: One-Way ANOVA
369(3)
9.12 Multivariate: One-Way MANOVA
372(7)
9.13 Multivariate: One-Way MANCOVA
379(2)
10 Computing Power for Univariate and Multivariate GLM 381(32)
10.1 Introduction
381(2)
10.2 Power for Univariate GLMs
383(1)
10.3 Estimating Power, Sample Size, and Effect Size for the GLM
384(4)
10.3.1 Power and Sample Size
384(1)
10.3.2 Effect Size
385(3)
10.4 Power and Sample Size Based on Interval-Estimation
388(2)
10.5 Calculating Power and Sample Size for Some Mixed Models
390(10)
10.5.1 Random One-Way ANOVA Design
390(6)
10.5.2 Two Factor Mixed Nested ANOVA Design
396(4)
10.6 Power for Multivariate GLMs
400(1)
10.7 Power and Effect Size Analysis for Univariate GLMs
401(4)
10.7.1 One-Way ANOVA
401(2)
10.7.2 Three-Way ANOVA
403(2)
10.7.3 One-Way ANCOVA Design with Two Covariates
405(1)
10.8 Power and Sample Size Based on Interval-Estimation
405(4)
10.8.1 One-Way ANOVA
407(2)
10.9 Power Analysis for Multivariate GLMs
409(4)
10.9.1 Two Groups
409(1)
10.9.2 Repeated Measures Design
409(4)
11 Two-Level Hierarchical Linear Models 413(42)
11.1 Introduction
413(1)
11.2 Two-Level Hierarchical Linear Models
413(11)
11.3 Random Coefficient Model: One Population
424(7)
11.4 Random Coefficient Model: Several Populations
431(9)
11.5 Mixed Model Repeated Measures
440(2)
11.6 Mixed Model Repeated Measures with Changing Covariates
442(1)
11.7 Application: Two-Level Hierarchical Linear Models
443(12)
12 Incomplete Repeated Measurement Data 455(24)
12.1 Introduction
455(1)
12.2 Missing Mechanisms
456(1)
12.3 FGLS Procedure
457(3)
12.4 ML Procedure
460(1)
12.5 Imputations
461(3)
12.5.1 EM Algorithm
462(1)
12.5.2 Multiple Imputation
463(1)
12.6 Repeated Measures Analysis
464(1)
12.7 Repeated Measures with Changing Covariates
464(3)
12.8 Random Coefficient Model
467(4)
12.9 Growth Curve Analysis
471(8)
13 Structural Equation Modeling 479(32)
13.1 Introduction
479(2)
13.2 Model Notation
481(8)
13.3 Estimation
489(5)
13.4 Model Fit in Practice
494(2)
13.5 Model Modification
496(2)
13.6 Summary
498(1)
13.7 Path Analysis
499(4)
13.8 Confirmatory Factor Analysis
503(1)
13.9 General SEM
503(8)
References 511(26)
Author Index 537(8)
Subject Index 545


Kevin Kim, Neil Timm
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
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