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E-raamat: Missing Data Analysis in Practice

2.67/5 (6 hinnangut Goodreads-ist)
(University of Michigan, Institute of Social Research, Ann Arbor, USA)
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Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.

The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.

Arvustused

" This book describes, both in simple and technical terms, several easy-to-implement methods, discusses the underlying assumptions of each method clearly and provides means for assessing these assumptions supplemented with practical implementations. In short, the author has written an interesting and highly valuable book, which intends to serve the need of graduate students and statistical practitioners, working, for instance, with data from sample surveys or from longitudinal studies or from survival studies." Apostolos Batsidis, in the Zentralblatt MATH, February 2018

" This monograph presents a very readable introduction to methods for dealing with missing values in survey data. Its focus is firmly on practical aspects, leaving the underlying theory for further reading and study of the many references, which include groundbreaking work by Donald Rubin. The first four chapters are an effective exercise in convincing the reader that some of the simple-minded approaches are deficient. The narrative motivates methods that have integrity, developing them step by step to address the identified deficiencies. The arguments presented are illustrated on examples and survey programs. They promote multiple imputation as a general method and elaborate the conditions under which they are appropriate. Every chapter is concluded with biographical notes and a set of exercises, some of which can be developed into substantial projects. Altogether, a wealth of experience, wisdom, insight and sage advice is packed into a thin volume." Nicholas T. Longford, in Mathematical Reviews Clippings, November 2017

" The presentation in Missing Data Analysis in Practice has the feel of well-honed lecture material It should be understood that a text that barely clears 200 pages is not going to cover the entirety of what specialists need to know to become expert on the topic. But as an overview of the field,

List of Tables
xiii
List of Figures
xv
Preface xvii
1 Basic Concepts
1(26)
1.1 Introduction
1(1)
1.2 Definition of Missing Values
2(1)
1.3 Missing Data Pattern
3(1)
1.4 Missing Data Mechanism
4(3)
1.5 Problems with Complete-Case Analysis
7(2)
1.6 Analysis Approaches
9(4)
1.7 Basic Statistical Concepts
13(6)
1.8 A Chuckle or Two
19(2)
1.9 Bibliographic Note
21(2)
1.10 Exercises
23(4)
2 Weighting Methods
27(24)
2.1 Motivation
27(2)
2.2 Adjustment Cell Method
29(1)
2.3 Response Propensity Model
29(3)
2.4 Example
32(5)
2.5 Impact of Weights on Population Mean Estimates
37(2)
2.6 Post-Stratification
39(5)
2.6.1 Post-Stratification Weights
39(1)
2.6.2 Raking
40(2)
2.6.3 Post-stratified Estimator
42(2)
2.7 Survey Weights
44(1)
2.8 Alternative to Weighted Analysis
45(2)
2.9 Inverse Probability Weighting
47(1)
2.10 Bibliographic Note
47(2)
2.11 Exercises
49(2)
3 Imputation
51(26)
3.1 Generation of Plausible Values
53(2)
3.2 Hot Deck Imputation
55(4)
3.2.1 Connection with Weighting
57(1)
3.2.2 Bayesian Modification
58(1)
3.3 Model Based Imputation
59(4)
3.4 Example
63(4)
3.5 Sequential Regression Imputation
67(8)
3.5.1 Details
69(2)
3.5.2 Handling Restrictions
71(2)
3.5.3 Model Fitting Issues
73(2)
3.6 Bibliographic Note
75(1)
3.7 Exercises
76(1)
4 Multiple Imputation
77(22)
4.1 Introduction
77(1)
4.2 Basic Combining Rule
77(2)
4.3 Multivariate Hypothesis Testing
79(1)
4.4 Combining Test Statistics
80(2)
4.5 Basic Theory of Multiple Imputation
82(1)
4.6 Extended Combining Rules
83(3)
4.6.1 Transformation
84(1)
4.6.2 Nonnormal Approximation
85(1)
4.7 Some Practical Issues
86(3)
4.7.1 Number of Imputations
86(1)
4.7.2 Diagnostics
87(1)
4.7.3 To Impute or Not to Impute
88(1)
4.8 Revisiting Examples
89(2)
4.8.1 Data in Table 1.1
89(1)
4.8.2 Case-Control Study
90(1)
4.9 Example: St. Louis Risk Research Project
91(4)
4.10 Bibliographic Note
95(1)
4.11 Exercises
95(4)
5 Regression Analysis
99(22)
5.1 General Observations
99(4)
5.1.1 Imputation Issues
99(4)
5.2 Revisiting St. Louis Risk Research Example
103(2)
5.3 Analysis of Variance
105(8)
5.3.1 Complete Data Analysis
106(1)
5.3.1.1 Partitioning of Sum of Squares
106(2)
5.3.1.2 Regression Formulation
108(1)
5.3.2 ANOVA with Missing Values
108(1)
5.3.2.1 Combining Sums of Squares
109(1)
5.3.2.2 Regression Formulation with Missing Values
110(1)
5.3.3 Example
110(2)
5.3.4 Extensions
112(1)
5.4 Survival Analysis Example
113(4)
5.5 Bibliographic Note
117(1)
5.6 Exercises
117(4)
6 Longitudinal Analysis with Missing Values
121(24)
6.1 Introduction
121(3)
6.2 Imputation Model Assumption
124(6)
6.2.1 Completed as Randomized
126(2)
6.2.2 Completed as Control
128(1)
6.2.3 Completed as Stable
129(1)
6.3 Example
130(5)
6.3.1 Completed as Randomized: Maximum Likelihood Analysis
130(3)
6.3.2 Multiple Imputation: Completed as Randomized
133(2)
6.3.3 Multiple Imputation: Completed as Control
135(1)
6.4 Practical Issues
135(1)
6.5 Weighting Methods
136(3)
6.6 Binary Example
139(3)
6.7 Bibliographic Note
142(1)
6.8 Exercises
143(2)
7 Nonignorable Missing Data Mechanisms
145(10)
7.1 Modeling Framework
145(1)
7.2 EM-Algorithm
146(2)
7.3 Inference under Selection Model
148(3)
7.4 Inference under Mixture Model
151(1)
7.5 Example
151(1)
7.6 Practical Considerations
152(1)
7.7 Bibliographic Note
153(1)
7.8 Exercises
154(1)
8 Other Applications
155(20)
8.1 Measurement Error
155(4)
8.2 Combining Information from Multiple Data Sources
159(1)
8.3 Bayesian Inference from Finite Population
160(3)
8.4 Causal Inference
163(2)
8.5 Disclosure Limitation
165(4)
8.6 Bibliographic Note
169(1)
8.7 Problems
170(5)
9 Other Topics
175(12)
9.1 Uncongeniality and Multiple Imputation
175(2)
9.2 Multiple Imputation for Complex Surveys
177(2)
9.3 Missing Values by Design
179(1)
9.4 Replication Method for Variance Estimation
180(2)
9.5 Final Thoughts
182(1)
9.6 Bibliographic Note
183(1)
9.7 Exercises
184(3)
Bibliography 187(18)
Index 205
Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a range of statistical and public health journals. His research interests include applied regression analysis, linear models, design of experiments, sample survey methods, and Bayesian inference.
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