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纵向数据分析方法与应用（英文版）

作者: 刘宪著

出版社: 高等教育出版社

出版时间: 2015-10

版次: 1

ISBN: 9787040436884

定价: 89.00

装帧: 平装

开本: 16开

纸张: 胶版纸

页数: 511页

字数: 840千字

正文语种: 英语

丛书: 应用统计学丛书

分类: 社会文化

2 张插图图片

9人买过

　　《纵向数据分析方法与应用（英文版）》旨在系统地介绍纵向数据分析的基本概念、理论设定和应用步骤，重点通过SAS计算机程序对实际数据进行分析，从而深入浅出地描述纵向数据分析的各类模型。书中涉及的统计方法包括各类描述性估算法、线性混合效应模型、随机效应的统计推断及估计、残差协方差结构类型、广义线性混合效应模型的理论性描述、二分组结局混合效应模型、多结局混合效应模型、各类潜变量发展模型、缺损数据分类及分析方法以及一些纵向分析方面的专题研究。
　　《纵向数据分析方法与应用（英文版）》着重于各类纵向分析模型的实际运用，而不拘泥于模型的纯理论推论，从而使对纵向数据分析有兴趣的科研人员以及大学生、研究生从中受益。　　刘宪，密歇根大学社会学博士（1991）。现任美国国防医科大学（Uniformed Services University of the Health Sciences）精神病学系教授、高级研究员及美国沃特·里德国家军事医学中心（Walter Reed National Military Medical Center）研究员、高级统计师。著有专著《生存分析：模型与应用》及《纵向数据分析方法与应用》。所发表论文与著作在国际上被大量引用。曾多次获得美国国立卫生研究院（National Institutes of Health）、退伍军人事务部及密歇根大学研究基金。主要研究领域为纵向数据分析、生存分析与死亡率交叉研究、老年人口学、创伤事件与精神疾病。 Biography
Preface
CHAPTER 1 Intr0dueti0n
1．1 What is Longitudinal Data Analysis?
1．2 History of Longitudinal Analysis and its Progress
1．3 Longitudinal Data Structures
1．3．1 Multivariate Data Structure
1．3．2 Univariate Data Structure
1．3．3 Balanced and Unbalanced Longitudinal Data
1．4 Missing Data Patterns and Mechanisms
1．5 Sources of Correlation in Longitudinal Processes
1．6 Time Scale and the Number of Time Points
1．7 Basic Expressions of Longitudinal Modeling
1．8 Organization of the Book and Data Used for Illustrations
1．8．1 Randomized Controlled Clinical Trial on the Effectiveness of Acupuncture Treatment on PTSD
1．8．2 Asset and Health Dynamics Among the Oldest Old （AHEAD）

CHAPTER 2 Traditional Methods of Longitudinal Data Analysis．．
2．1 Descriptive Approaches
2．1．1 Time Plots of Trends
2．1．2 Paired t-Test
2．1．3 Effect Size Between Two Means and its Confidence Interval
2．1．4 Empirical Illustration： Descriptive Analysis on the Effectiveness of Acupuncture Treatment in Reduction of PTSD Symptom Severity
2．2 Repeated Measures ANOVA
2．2．1 Specifications of One-Factor ANOVA
2．2．2 One-Factor Repeated Measures ANOVA
2．2．3 Specifications of Two-Factor Repeated Measures ANOVA
2．2．4 Empirical Illustration： A Two-Factor Repeated Measures ANOVA - The Effectiveness of Acupuncture Treatment on PCL Revisited
2．3 Repeated Measures MANOVA
2．3．1 General MANOVA
2．3．2 Hypothesis Testing on Effects in MANOVA
2．3．3 Repeated Measures MANOVA
2．3．4 Empirical Illustration： A Two-Factor Repeated Measures MANOVA on the Effectiveness of Acupuncture Treatment on Two Psychiatric Disorders
2．4 Summary

CHAPTER 3 Linear Mixed-Effects Models
3．1 Introduction of Linear Mixed Models： Three Cases
3．1．1 Case I： One-Factor Linear Mixed Model with Random Intercept
3．1．2 Case II： linear Mixed Model with Random Intercept and Random Slope
3．1．3 Case III： Linear Mixed Model with Random Effects and Three Covariates
3．2 Formalization of Linear Mixed Models
3．2．1 General Specification of Linear Mixed Models
3．2．2 Variance-Covariance Matrix and Intraindividual Correlation
3．2．3 Formalization of Variance-Covariance Components
3．3 Inference and Estimation of Fixed Effects In Linear
Mixed Models
3．3．1 Maximum Likelihood Methods
3．3．2 Statistical Inference and Hypothesis Testing on Fixed Effects
3．3．3 Missing Data
3．4 Trend Analysis
3．4．1 Polynomial Time Functins
3．4．2 Methods to Reduce Collinearity in Polynomial Time Terms
3．4．3 Numeric Checks on Polynomial Time Functions
3．5 Empirical Illustrations： Application of Two Linear
Mixed Models
3．5．1 Linear Mixed Model on Effectiveness of Acupuncture Treatment on PCL Score
3．5．2 Linear Mixed Model on Marital Status and Disability Severity in Older Americans
3．6 Summary
……

CHAPTER 4 Restricted Maximum Likelihood and Inference of Random Effects in Linear Mixed Models
CHAPTER 5 Patterns of Residual Covariance Structure
CHAPTER 6 Residual and Influence Diagnostics
CHAPTER 7 Special Topics on Linear Mixed Models
CHAPTER 8 Generalized Linear Mixed Models on Nonlinear Longitudinal Data
CHAPTER 9 Generalized Estimating Equations （GEEs） Models．
CHAPTER 10 Mixed-Effects Regression Model for Binary Longitudinal Data
CHAPTER 11 Mixed-Effects Multinomial Logit Model for Nominal Outcomes
CHAPTER 12 Longitudinal Transition Models for Categorical Response Data
CHAPTER 13 Latent Growth， Latent Growth Mixture， and Group-Based Models
CHAPTER 14 Methods for Handling Missing Data
Appendix A Orthogonal Polynomials
Appendix B The Delta Method
Appendix C Quasi-Likelihood Functions and Properties
Appendix D Model Specification and SAS Program for Random Coefficient Multinomial Logit Model on Health State Among Older Americans
References
Subject Index
内容简介:
　　《纵向数据分析方法与应用（英文版）》旨在系统地介绍纵向数据分析的基本概念、理论设定和应用步骤，重点通过SAS计算机程序对实际数据进行分析，从而深入浅出地描述纵向数据分析的各类模型。书中涉及的统计方法包括各类描述性估算法、线性混合效应模型、随机效应的统计推断及估计、残差协方差结构类型、广义线性混合效应模型的理论性描述、二分组结局混合效应模型、多结局混合效应模型、各类潜变量发展模型、缺损数据分类及分析方法以及一些纵向分析方面的专题研究。
　　《纵向数据分析方法与应用（英文版）》着重于各类纵向分析模型的实际运用，而不拘泥于模型的纯理论推论，从而使对纵向数据分析有兴趣的科研人员以及大学生、研究生从中受益。
作者简介:
　　刘宪，密歇根大学社会学博士（1991）。现任美国国防医科大学（Uniformed Services University of the Health Sciences）精神病学系教授、高级研究员及美国沃特·里德国家军事医学中心（Walter Reed National Military Medical Center）研究员、高级统计师。著有专著《生存分析：模型与应用》及《纵向数据分析方法与应用》。所发表论文与著作在国际上被大量引用。曾多次获得美国国立卫生研究院（National Institutes of Health）、退伍军人事务部及密歇根大学研究基金。主要研究领域为纵向数据分析、生存分析与死亡率交叉研究、老年人口学、创伤事件与精神疾病。
目录:
Biography
Preface
CHAPTER 1 Intr0dueti0n
1．1 What is Longitudinal Data Analysis?
1．2 History of Longitudinal Analysis and its Progress
1．3 Longitudinal Data Structures
1．3．1 Multivariate Data Structure
1．3．2 Univariate Data Structure
1．3．3 Balanced and Unbalanced Longitudinal Data
1．4 Missing Data Patterns and Mechanisms
1．5 Sources of Correlation in Longitudinal Processes
1．6 Time Scale and the Number of Time Points
1．7 Basic Expressions of Longitudinal Modeling
1．8 Organization of the Book and Data Used for Illustrations
1．8．1 Randomized Controlled Clinical Trial on the Effectiveness of Acupuncture Treatment on PTSD
1．8．2 Asset and Health Dynamics Among the Oldest Old （AHEAD）

CHAPTER 2 Traditional Methods of Longitudinal Data Analysis．．
2．1 Descriptive Approaches
2．1．1 Time Plots of Trends
2．1．2 Paired t-Test
2．1．3 Effect Size Between Two Means and its Confidence Interval
2．1．4 Empirical Illustration： Descriptive Analysis on the Effectiveness of Acupuncture Treatment in Reduction of PTSD Symptom Severity
2．2 Repeated Measures ANOVA
2．2．1 Specifications of One-Factor ANOVA
2．2．2 One-Factor Repeated Measures ANOVA
2．2．3 Specifications of Two-Factor Repeated Measures ANOVA
2．2．4 Empirical Illustration： A Two-Factor Repeated Measures ANOVA - The Effectiveness of Acupuncture Treatment on PCL Revisited
2．3 Repeated Measures MANOVA
2．3．1 General MANOVA
2．3．2 Hypothesis Testing on Effects in MANOVA
2．3．3 Repeated Measures MANOVA
2．3．4 Empirical Illustration： A Two-Factor Repeated Measures MANOVA on the Effectiveness of Acupuncture Treatment on Two Psychiatric Disorders
2．4 Summary

CHAPTER 3 Linear Mixed-Effects Models
3．1 Introduction of Linear Mixed Models： Three Cases
3．1．1 Case I： One-Factor Linear Mixed Model with Random Intercept
3．1．2 Case II： linear Mixed Model with Random Intercept and Random Slope
3．1．3 Case III： Linear Mixed Model with Random Effects and Three Covariates
3．2 Formalization of Linear Mixed Models
3．2．1 General Specification of Linear Mixed Models
3．2．2 Variance-Covariance Matrix and Intraindividual Correlation
3．2．3 Formalization of Variance-Covariance Components
3．3 Inference and Estimation of Fixed Effects In Linear
Mixed Models
3．3．1 Maximum Likelihood Methods
3．3．2 Statistical Inference and Hypothesis Testing on Fixed Effects
3．3．3 Missing Data
3．4 Trend Analysis
3．4．1 Polynomial Time Functins
3．4．2 Methods to Reduce Collinearity in Polynomial Time Terms
3．4．3 Numeric Checks on Polynomial Time Functions
3．5 Empirical Illustrations： Application of Two Linear
Mixed Models
3．5．1 Linear Mixed Model on Effectiveness of Acupuncture Treatment on PCL Score
3．5．2 Linear Mixed Model on Marital Status and Disability Severity in Older Americans
3．6 Summary
……

CHAPTER 4 Restricted Maximum Likelihood and Inference of Random Effects in Linear Mixed Models
CHAPTER 5 Patterns of Residual Covariance Structure
CHAPTER 6 Residual and Influence Diagnostics
CHAPTER 7 Special Topics on Linear Mixed Models
CHAPTER 8 Generalized Linear Mixed Models on Nonlinear Longitudinal Data
CHAPTER 9 Generalized Estimating Equations （GEEs） Models．
CHAPTER 10 Mixed-Effects Regression Model for Binary Longitudinal Data
CHAPTER 11 Mixed-Effects Multinomial Logit Model for Nominal Outcomes
CHAPTER 12 Longitudinal Transition Models for Categorical Response Data
CHAPTER 13 Latent Growth， Latent Growth Mixture， and Group-Based Models
CHAPTER 14 Methods for Handling Missing Data
Appendix A Orthogonal Polynomials
Appendix B The Delta Method
Appendix C Quasi-Likelihood Functions and Properties
Appendix D Model Specification and SAS Program for Random Coefficient Multinomial Logit Model on Health State Among Older Americans
References
Subject Index

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纵向数据分析方法与应用（英文版）

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