请选择 进入手机版 | 继续访问电脑版
楼主: wakacheung
1230 2

[书籍介绍] General Linear Models Theory using SAS software [推广有奖]

  • 1关注
  • 2粉丝

讲师

12%

还不是VIP/贵宾

-

威望
0
论坛币
4953 个
通用积分
102.0832
学术水平
11 点
热心指数
4 点
信用等级
1 点
经验
106 点
帖子
66
精华
0
在线时间
750 小时
注册时间
2009-9-26
最后登录
2023-10-8

wakacheung 发表于 2017-3-20 22:23:42 |显示全部楼层 |坛友微信交流群
相似文件 换一批

+2 论坛币
k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群
赵安豆老师微信:zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

感谢您参与论坛问题回答

经管之家送您两个论坛币!

+2 论坛币
Univariate and Multivariate General Linear Models Theory and Applications Using .rar (2.86 MB, 需要: 10 个论坛币) 本附件包括:
  • Univariate and Multivariate General Linear Models Theory and Applications Using SAS software.pdf

PDF高清文字版

Contents

Preface xi
1 Overview of the General Linear Model 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 The General Linear Model . . . . . . . . . . . . . . . . . . . . . . 1
1.3 The Restricted General Linear Model . . . . . . . . . . . . . . . . 3
1.4 The Multivariate Normal Distribution . . . . . . . . . . . . . . . . 4
1.5 Elementary Properties of Normal Random Variables . . . . . . . . . 8
1.6 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.7 Generating Multivariate Normal Data . . . . . . . . . . . . . . . . 10
1.8 Assessing Univariate Normality . . . . . . . . . . . . . . . . . . . 11
1.8.1 Normally and Nonnormally Distributed Data . . . . . . . . 12
1.8.2 Real Data Example . . . . . . . . . . . . . . . . . . . . . . 15
1.9 Assessing Multivariate Normality with Chi-square Plots . . . . . . . 15
1.9.1 Multivariate Normal Data . . . . . . . . . . . . . . . . . . 18
1.9.2 Real Data Example . . . . . . . . . . . . . . . . . . . . . . 19
1.10 Using SAS INSIGHT . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.10.1 Ramus Bone Data . . . . . . . . . . . . . . . . . . . . . . 19
1.10.2 Risk-taking Behavior Data . . . . . . . . . . . . . . . . . . 21
1.11 Three-Dimensional Plots . . . . . . . . . . . . . . . . . . . . . . . 23

2 Unrestricted General Linear Models 25
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2 Linear Models without Restrictions . . . . . . . . . . . . . . . . . . 25
2.3 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4 Simultaneous Inference . . . . . . . . . . . . . . . . . . . . . . . . 28
2.5 Multiple Linear Regression . . . . . . . . . . . . . . . . . . . . . . 30
2.5.1 Classical and Normal Regression Models . . . . . . . . . . 31
2.5.2 Random Classical and Jointly Normal Regression Models . 42
2.6 Linear Mixed Models . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.7 One-Way Analysis of Variance . . . . . . . . . . . . . . . . . . . . 53
2.7.1 Unrestricted Full Rank One-way Design . . . . . . . . . . . 54
2.7.2 Simultaneous Inference for the One-Way Design . . . . . . 56
2.7.3 Multiple Testing . . . . . . . . . . . . . . . . . . . . . . . 58
2.8 Multiple Linear Regression:Calibration . . . . . . . . . . . . . . . 58
2.8.1 Multiple Linear Regression: Prediction . . . . . . . . . . . 68
2.9 Two-way Nested Designs . . . . . . . . . . . . . . . . . . . . . . . 70
2.10 Intraclass Covariance Models . . . . . . . . . . . . . . . . . . . . . 72

3 Restricted General Linear Models 77
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.2 Estimation and Hypothesis Testing . . . . . . . . . . . . . . . . . . 77
3.3 Two-Way Factorial Design without Interaction . . . . . . . . . . . . 79
3.4 Latin Square Designs . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.5 Repeated Measures Designs . . . . . . . . . . . . . . . . . . . . . 89
3.5.1 Univariate Mixed ANOVA Model, Full Rank Representation
for a Split Plot Design . . . . . . . . . . . . . . . . . . . . 90
3.5.2 Univariate Mixed Linear Model, Less than Full Rank Representation   95
3.5.3 Test for Equal Covariance Matrices and for Circularity . . . 97
3.6 Analysis of Covariance . . . . . . . . . . . . . . . . . . . . . . 100
3.6.1 ANCOVA with One Covariate . . . . . . . . . . . . . . . . 102
3.6.2 ANCOVA with Two Covariates . . . . . . . . . . . . . . . 104
3.6.3 ANCOVA Nested Designs . . . . . . . . . . . . . . . . . . 106

4 Weighted General Linear Models 109
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .109
4.2 Estimation and Hypothesis Testing . . . . . . . . . . . . . . . . . 110
4.3 OLSE versus FGLS . . . . . . . . . . . . . . . . . . . . . . . . . 113
4.4 General Linear Mixed Model Continued . . . . . . . . . . . . . . . 114
4.4.1 Example: Repeated Measures Design . . . . . . . . . . . . 117
4.4.2 Estimating the df for the F statistic in GLMMs . . . . . . . 118
4.5 Maximum Likelihood Estimation and Fisher’s Information Matrix . 119
4.6 WLSE for data Heteroscedasticity . . . . . . . . . . . . . . . . . . 121
4.7 WLSE for Correlated Errors . . . . . . . . . . . . . . . . . . . . . 124
4.8 FGLS for Categorical Data . . . . . . . . . . . . . . . . . . . . . . 127
4.8.1 Overview of the Categorical Data Model . . . . . . . . . . 127
4.8.2 Marginal Homogeneity . . . . . . . . . . . . . . . . . . . . 130
4.8.3 Homogeneity of Proportions . . . . . . . . . . . . . . . . . 132
4.8.4 Independence . . . . . . . . . . . . . . . . . . . . . . . . . 138
4.8.5 Univariate Mixed Linear Model, Less than Full Rank Representation . . 141

5 Multivariate General Linear Models 143
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
5.2 Developing the Model . . . . . . . . . . . . . . . . . . . . . . . . 143
5.3 Estimation Theory and Hypothesis Testing . . . . . . . . . . . . . . 145
5.4 Multivariate Regression . . . . . . . . . . . . . . . . . . . . . . .152
5.5 Classical and Normal Multivariate Linear Regression Models . . . . 153
5.6 Jointly Multivariate Normal Regression Model . . . . . . . . . . . 163
5.7 Multivariate Mixed Models and the Analysis of Repeated Measurements. 171
5.8 Extended Linear Hypotheses . . . . . . . . . . . . . . . . . . . . . 176
5.9 Multivariate Regression: Calibration and Prediction . . . . . . . . .182
5.9.1 Fixed X . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
5.9.2 Random X . . . . . . . . . . . . . . . . . . . . . . . . . . 185
5.9.3 Random X, Prediction . . . . . . . . . . . . . . . . . . . . 186
5.9.4 Overview - Candidate Model . . . . . . . . . . . . . . . . . 186
5.9.5 Prediction and Shrinkage . . . . . . . . . . . . . . . . . . . 187
5.10 Multivariate Regression: Influential Observations . . . . . . . .189
5.10.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 191
5.11 Nonorthogonal MANOVA designs . . . . . . . . . . . . . . . . . . 192
5.11.1 Unweighted Analysis . . . . . . . . . . . . . . . . . . . . . 197
5.11.2 Weighted Analysis . . . . . . . . . . . . . . . . . . . . . . 198
5.12 MANCOVA Designs . . . . . . . . . . . . . . . . . . . . . . . . . 200
5.12.1 Overall tests . . . . . . . . . . . . . . . . . . . . . . . . . 200
5.12.2 Tests of Additional Information . . . . . . . . . . . . . . . 203
5.12.3 Results and Interpretation . . . . . . . . . . . . . . . . . . 204
5.13 Stepdown Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 206
5.14 Repeated Measures Analysis . . . . . . . . . . . . . . . . . . . . . 208
5.14.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 209
5.15 Extended Linear Hypotheses . . . . . . . . . . . . . . . . . . . . . 216
5.15.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 219

6 Doubly Multivariate Linear Model 223
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .223
6.2 Classical Model Development . . . . . . . . . . . . . . . . . . . . 223
6.3 Responsewise Model Development . . . . . . . . . . . . . . . . . . 226
6.4 The Multivariate Mixed Model . . . . . . . . . . . . . . . . . . . . 227
6.5 Double Multivariate and Mixed Models . . . . . . . . . . . . . . . 231

7 The Restricted MGLM and the Growth Curve Model 243
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
7.2 The Restricted Multivariate General Linear Model . . . . . . . . . . 243
7.3 The GMANOVA Model . . . . . . . . . . . . . . . . . . . . . . . 247
7.4 Canonical Form of the GMANOVA Model . . . . . . . . . . . . . . 253
7.5 Restricted Nonorthogonal Three-Factor Factorial MANOVA . . . . 259
7.5.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 269
7.6 Restricted Intraclass Covariance Design . . . . . . . . . . . . . . 269
7.6.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 275
7.7 Growth Curve Analysis . . . . . . . . . . . . . . . . . . . . . . . . 279
7.7.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 283
7.8 Multiple Response Growth Curves . . . . . . . . . . . . . . . . . . 289
7.8.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 290
7.9 Single Growth Curve . . . . . . . . . . . . . . . . . . . . . . .294
7.9.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 294

8 The SUR Model and the Restricted GMANOVA model 297
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 297
8.2 The MANOVA-GMANOVA Model . . . . . . . . . . . . . . . . . 297
8.3 Tests of Fit . . . . . . . . . . . . . . . . . . . . . . . . . .303
8.4 Sum of Profiles and CGMANOVA Models . . . . . . . . . . . . . . 305
8.5 The SUR Model . . . . . . . . . . . . . . . . . . . . . . . . . .307
8.6 The Restricted GMANOVA Model . . . . . . . . . . . . . . . . . . 314
8.7 GMANOVA-SUR: One Population . . . . . . . . . . . . . . . . . . 317
8.7.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 317
8.8 GMANOVA-SUR: Several Populations . . . . . . . . . . . . . . . 319
8.8.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 319
8.9 SUR Model . . . . . . . . . . . . . . . . . . . . . . . . . . . .319
8.9.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 323
8.10 Two-Period Crossover Design with Changing Covariates . . . . . . 328
8.10.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 329
8.11 Repeated Measurements with Changing Covariates . . . . . . . . . 334
8.11.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 335
8.12 MANOVA-GMANOVA Model . . . . . . . . . . . . . . . . . . . . 337
8.12.1 Results and interpretation . . . . . . . . . . . . . . . . . . 338
8.13 CGMANOVA Model . . . . . . . . . . . . . . . . . . . . . . . . . 344
8.13.1 Results and Interpretation . . . . . . . . . . . . . . . . . . 346

9 Simultaneous Inference Using Finite Intersection Tests 349
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349
9.2 Finite Intersection Tests . . . . . . . . . . . . . . . . . . . . . . 349
9.3 Finite Intersection Tests of Univariate Means . . . . . . . . . . . . 350
9.4 Finite Intersection Tests for Linear Models . . . . . . . . . . . . . 354
9.5 A Comparisons of Some Tests of Univariate Means . . . . . . . . . 355
9.5.1 Single-Step Methods . . . . . . . . . . . . . . . . . . . . . 355
9.5.2 Stepdown Methods . . . . . . . . . . . . . . . . . . . . . . 357
9.6 Analysis of Means Analysis . . . . . . . . . . . . . . . . . . . . . 358
9.7 Simultaneous Test Procedures for Mean Vectors . . . . . . . . . . . 360
9.8 Finite Intersection Test of Mean Vectors . . . . . . . . . . . . . . . 362
9.9 Finite Intersection Test of Mean Vectors with Covariates . . . . . . 366
9.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368
9.11 Univariate: One-way ANOVA . . . . . . . . . . . . . . . . . . . . 369
9.12 Multivariate: One-way MANOVA . . . . . . . . . . . . . . . . . . 372
9.13 Multivariate: One-way MANCOVA . . . . . . . . . . . . . . . . . 379

10 Computing Power for Univariate and Multivariate GLM 381
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .381
10.2 Power for Univariate GLMs . . . . . . . . . . . . . . . . . . . . . 383
10.3 Estimating Power, Sample Size, and Effect Size for the GLM . . . . 384
10.3.1 Power and Sample Size . . . . . . . . . . . . . . . . . . . . 384
10.3.2 Effect Size . . . . . . . . . . . . . . . . . . . . . . . . . . 385
10.4 Power and Sample Size based upon Interval-Estimation . . . . . . . 388
10.5 Calculating Power and Sample Size for Some Mixed Models . . . . 390
10.5.1 Random One-Way ANOVA Design . . . . . . . . . . . . . 390
10.5.2 Two Factor Mixed Nested ANOVA Design . . . . . . . . . 396
10.6 Power for Multivariate GLMs . . . . . . . . . . . . . . . . . . . . 400
10.7 Power and Effect Size Analysis for Univariate GLMs . . . . . . . . 401
10.7.1 One-Way ANOVA . . . . . . . . . . . . . . . . . . . . . . 401
10.7.2 Three-Way ANOVA . . . . . . . . . . . . . . . . . . . . . 403
10.7.3 One-Way ANCOVA Design with two covariates . . . . . . 405
10.8 Power and Sample Size based upon Interval-Estimation . . . . . . . 405
10.8.1 One-Way ANOVA . . . . . . . . . . . . . . . . . . . . . . 407
10.9 Power Analysis for Multivariate GLMs . . . . . . . . . . . . . . . 409
10.9.1 Two Groups . . . . . . . . . . . . . . . . . . . . . . . . . . 409
10.9.2 Repeated Measures Design . . . . . . . . . . . . . . . . . . 409

11 Two-level Hierarchical Linear Models 413
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413
11.2 Two-level Hierarchical Linear Models . . . . . . . . . . . . . . . . 413
11.3 Random Coefficient Model: One Population . . . . . . . . . . . . . 424
11.4 Random Coefficient Model: Several Populations . . . . . . . . . . . 435
11.5 Mixed Model Repeated Measures . . . . . . . . . . . . . . . . . . 440
11.6 Mixed Model Repeated Measures with Changing Covariates . . . . 442
11.7 Two-Level Hierarchical Linear Models . . . . . . . . . . . . . . . . 443

12 Incomplete Repeated Measurement Data 455
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455
12.2 Missing Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . 456
12.3 An FGLS Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 457
12.4 An ML Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . .460
12.5 Imputations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .461
12.5.1 EM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 462
12.5.2 Multiple Imputation . . . . . . . . . . . . . . . . . . . . . 463
12.6 Repeated Measures Analysis . . . . . . . . . . . . . . . . . . . . . 464
12.7 Repeated Measures with Changing Covariates . . . . . . . . . . . . 464
12.8 Random Coefficient Model . . . . . . . . . . . . . . . . . . . . . . 467
12.9 Growth Curve Analysis . . . . . . . . . . . . . . . . . . . . . . . . 471

13 Structural Equation Modeling 479
13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .479
13.2 Model Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . 481
13.3 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489
13.4 Model Fit in Practice . . . . . . . . . . . . . . . . . . . . . . . .494
13.5 Model Modification . . . . . . . . . . . . . . . . . . . . . . . . . 496
13.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .498
13.7 Path Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . .499
13.8 Confirmatory Factor Analysis . . . . . . . . . . . . . . . . . . . . 503
13.9 General SEM . . . . . . . . . . . . . . . . . . . . . . . . . . . .  503
References
二维码

扫码加我 拉你入群

请注明:姓名-公司-职位

以便审核进群资格,未注明则拒绝

关键词:software General models Theory Linear software General Random

钱学森64 发表于 2017-3-20 23:12:33 |显示全部楼层 |坛友微信交流群
谢谢分享

使用道具

franky_sas 发表于 2017-3-21 09:34:11 |显示全部楼层 |坛友微信交流群

使用道具

您需要登录后才可以回帖 登录 | 我要注册

本版微信群
加好友,备注cda
拉您进交流群

京ICP备16021002-2号 京B2-20170662号 京公网安备 11010802022788号 论坛法律顾问:王进律师 知识产权保护声明   免责及隐私声明

GMT+8, 2024-3-29 01:38