Flexible imputation of missing data:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton [u.a.]
CRC Press
2012
|
| Series: | Chapman & Hall, CRC interdisciplinary statistics series
A Chapman & Hall book |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXV, 316 S. graph. Darst. |
| ISBN: | 9781439868249 |
Staff View
MARC
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| 245 | 1 | 0 | |a Flexible imputation of missing data |c Stef van Buuren |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2012 | |
| 300 | |a XXV, 316 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 0 | |a Chapman & Hall, CRC interdisciplinary statistics series | |
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Record in the Search Index
| _version_ | 1804148752579035136 |
|---|---|
| adam_text | Contents
Foreword
xvii
Preface
xix
About the Author
xxi
Symbol Description
xxiii
List of Algorithms
xxv
I Basics
1
1
Introduction
3
1.1
The problem of missing data
.................. 3
1.1.1
Current practice
..................... 3
1.1.2
Changing perspective on missing data
......... 5
1.2
Concepts of MCAR. MAR and MNAR
............ 6
1.3
Simple solutions that do not (always) work
.......... 8
1.3.1
Listwise deletion
..................... 8
1.3.2
Pairwise deletion
..................... 9
1.3.3
Alean
imputation
..................... 10
1.3.4
Regression imputation
.................. 11
1.3.5
Stochastic regression imputation
............ 13
1.3.6
LOCF and BOFC
.................... 14
1.3.7
Indicator method
..................... 15
1.3.8
Summary
......................... 15
1.4
Multiple imputation in a nutshell
............... 16
1.4.1
Procedure
......................... 16
1.4.2
Reasons to use multiple imputation
.......... 17
1.4.3
Example of multiple imputation
............
IS
1.5
Goal of the book
......................... 20
1.6
What the book does not cover
................. 20
1.6.1
Prevention
........................ 21
1.6.2
Weighting procedures
.................. 21
1.6.3
Likelihood-based approaches
.............. 22
1.7
Structure of the book
...................... 23
1.8
Exercises
............................. 23
Contents
Multiple
imputation
25
2.1 Historie
overview ........................
25
2.1.1
Imputation
........................ 25
2.1.2 Multiple
imputation
................... 25
2.1.3
The expanding literature on multiple imputation
... 27
2.2
Concepts in incomplete data
.................. 28
2.2.1
Incomplete data perspective
............... 28
2.2.2
Causes of missing data
.................. 29
2.2.3
Notation
.......................... 30
2.2.4
MCAR. MAR and MNAR again
............ 31
2.2.5
Ignorable
and nonignorable
*.............. 33
2.2.6
Implications of ignorability
............... 34
2.3
Why and when multiple imputation works
.......... 35
2.3.1
Goal of multiple imputation
............... 35
2.3.2
Three sources of variation
*............... 36
2.3.3
Proper imputation
.................... 38
2.3.4
Scope of the imputation model
............. 40
2.3.5
Variance ratios
*..................... 41
2.3.6
Degrees of freedom
* .................. 42
2.3.7
Numerical example
.................... 43
2.4
Statistical intervals and tests
.................. 44
2.4.1
Scalar or multi-parameter inference?
.......... 44
2.4.2
Scalar inference
...................... 44
2.5
Evaluation criteria
........................ 45
2.5.1
Imputation is not prediction
............... 45
2.5.2
Simulation designs and performance measures
..... 47
2.6
When to use multiple imputation
............... 48
2.7
How many imputations?
.................... 49
2.8
Exercises
............................. 51
Univariate missing data
53
3.1
How to generate multiple imputations
............. 53
3.1.1
Predict method
...................... 55
3.1.2
Predict
—
noise method
................. 55
3.1.3
Predict -t- noise
4-
parameter uncertainty
....... 55
3.1.4
A second predictor
.................... 56
3.1.5
Drawing from the observed data
............ 56
3.1.6
Conclusion
........................ 56
3.2
Imputation under the normal linear normal
.......... 57
3.2.1
Overview
......................... 57
3.2.2
Algorithms
*....................... 57
3.2.3
Performance
....................... 59
3.2.4
Generating MAR missing data
............. 63
3.2.5
Conclusion
........................ 64
3.3
Imputation under non-normal distributions
.......... 65
Contents
Xl
3.3.1
Overview
......................... 65
3.3.2
Imputation from the ¿-distribution
*.......... 66
3.3.3
Example
♦ ........................ 67
3.4
Predictive mean matching
................... 68
3.4.1
Overview
......................... 68
3.4.2
Computational details
*................. 70
3.4.3
Algorithm
♦ ....................... 73
3.4.4
Conclusion
........................ 74
3.5
Categorical data
......................... 75
3.5.1
Overview
......................... 75
3.5.2
Perfect prediction
*................... 76
3.6
Other data types
......................... 78
3.6.1
Count data
........................ 78
3.6.2
Semi-continuous data
.................. 79
3.6.3
Censored, truncated and rounded data
......... 79
3.7
Classification and regression trees
............... 82
3.7.1
Overview
......................... 82
3.7.2
Imputation using CART models
............ 83
3.8
Multilevel data
.......................... 84
3.8.1
Overview
......................... 84
3.8.2
Two formulations of the linear multilevel model
* . . 85
3.8.3
Computation
*...................... 86
3.8.4
Conclusion
........................ 87
3.9
Nonignorable missing data
................... 88
3.9.1
Overview
......................... 88
3.9.2
Selection model
...................... 89
3.9.3
Pattern-mixture model
.................. 90
3.9.4
Converting selection and pattern-mixture models
. . . 90
3.9.5
Sensitivity analysis
.................... 92
3.9.6
Role of sensitivity analysis
................ 93
3.10
Exercises
............................. 93
4
Multivariate missing data
95
4.1
Missing data pattern
...................... 95
4.1.1
Overview
......................... 95
4.1.2
Summary statistics
.................... 96
4.1.3
Influx and outHux
.................... 99
4.2
Issues in multivariate imputation
................ 101
4.3
Monotone data imputation
................... [02
4.3.1
Overview
......................... 102
4.3.2
Algorithm
......................... 103
4.4
Joint modeling
.......................... 105
4.4.1
Overview
......................... 105
4.4.2
Continuous data
*.................... 105
4.4.3
Categorical data
..................... 107
Contents
4.5
Fully conditional specification
................. 108
4.5.1
Overview
......................... 108
4.5.2
The MICE algorithm
................... 109
4.5.3
Performance
.......................
Ш
4.5.4
Compatibility
* .....................
Ill
4.5.5
Number of iterations
................... 112
4.5.6
Example of slow convergence
.............. 113
4.6
FCS and JM
........................... 116
4.6.1
Relations between FCS and JM
............. 116
4.6.2
Comparison
........................ 117
4.6.3
Illustration
........................ 117
4.7
Conclusion
............................ 121
4.8
Exercises
............................. 121
Imputation in practice
123
5.1
Overview of modeling choices
.................. 123
5.2
Ignorable
or nonignorable?
................... 125
5.3
Model form and predictors
................... 126
5.3.1
Model form
........................ 126
5.3.2
Predictors
......................... 127
5.4
Derived variables
......................... 129
5.4.1
Ratio of two variables
.................. 129
5.4.2
Sum scores
........................ 132
5.4.3
Interaction terms
..................... 133
5.4.4
Conditional imputation
................. 133
5.4.5
Compositional data
*.................. 136
5.4.6
Quadratic relations
* .................. 139
5.5
Algorithmic options
....................... 140
5.5.1
Visit sequence
...................... 140
5.5.2
Convergence
....................... 142
5.6
Diagnostics
............................ 146
5.6.1
Model fit versus distributional discrepancy
...... 146
5.6.2
Diagnostic graphs
.................... 146
5.7
Conclusion
............................ 151
5.8
Exercises
............................. 152
Analysis of imputed data
153
6.1
What to do with the imputed data?
.............. 153
6.1.1
Averaging and stacking the data
............ 153
6.1.2
Repeated analyses
.................... 154
6.2
Parameter pooling
........................ 155
6.2.1
Scalar inference of normal quantities
.......... 155
6.2.2
Scalar inference of non-normal quantities
....... 155
6.3
Statistical tests for multiple imputation
............ 156
6.3.1 Wald
test
*........................ 157
Contents
xiii
6.3.2
Likelihood
ratio
test
*.................. 157
6.3.3
x2-test
♦ ......................... 159
6.3.4
Custom hypothesis tests of model parameters
* .... 159
6.3.5
Computation
....................... 160
6.4
Stepwise model selection
.................... 162
6.4.1
Variable selection techniques
.............. 162
6.4.2
Computation
....................... 163
6.4.3
Model optimism
..................... 164
6.5
Conclusion
............................ 166
6.6
Exercises
............................. 166
II Case studies
169
7
Measurement issues
171
7.1
Too many columns
........................ 171
7.1.1
Scientific question
.................... 172
7.1.2
Leiden
85+
Cohort
.................... 172
7.1.3
Data exploration
..................... 173
7.1.4
Outflux
.......................... 175
7.1.5
Logged events
....................... 176
7.1.6
Quick predictor selection for wide data
......... 177
7.1.7
Generating the imputations
............... 179
7.1.8
A further improvement: Survival as predictor variable
180
7.1.9
Some guidance
...................... 181
7.2
Sensitivity analysis
....................... 182
7.2.1
Causes and consequences of missing data
....... 182
7.2.2
Scenarios
......................... 184
7.2.3
Generating imputations under the ¿-adjustment
. . . 185
7.2.4
Complete data analysis
................. 186
7.2.5
Conclusion
........................ 187
7.3
Correct prevalence estimates from self-reported data
..... 188
7.3.1
Description of the problem
............... 188
7.3.2
Don t count on predictions
...............
1{>9
7.3.3
The main idea
...................... 190
7.3.4
Data
............................ 191
7.3.5
Application
........................ 192
7.3.6
Conclusion
........................ 193
7.4
Enhancing comparability
.................... 194
7.4.1
Description of the problem
............... 194
7.4.2
Full dependence: Simple equating
............ 195
7.4.3
Independence: Imputation without a bridge study
. . 196
7.4.4
Fully dependent or independent?
............
19S
7.4.5
Imputation using a bridge study
............ 199
7.4.6
Interpretation
....................... 202
7.4.7
Conclusion
........................ 203
xiv Contents
7.5
Exercises
............................. 204
8
Selection issues
205
8.1
Correcting for selective drop-out
................ 205
8.1.1
POPS study:
19
years follow-up
............. 205
8.1.2
Characterization of the drop-out
............ 206
8.1.3
Imputation model
.................... 207
8.1.4
A degenerate solution
.................. 208
8.1.5
A better solution
..................... 210
8.1.6
Results
.......................... 211
8.1.7
Conclusion
........................ 211
8.2
Correcting for
nonresponse
................... 212
8.2.1
Fifth Dutch Growth Study
............... 212
8.2.2
Nonresponse
....................... 213
8.2.3
Comparison to known population totals
........ 213
8.2.4
Augmenting the sample
................. 214
8.2.5
Imputation model
.................... 215
8.2.6
Influence of
nonresponse
on final height
........ 217
8.2.7
Discussion
......................... 218
8.3
Exercises
............................. 219
9
Longitudinal data
221
9.1
Long and wide format
...................... 221
9.2
SE
Fireworks Disaster Study
.................. 223
9.2.1
Intention to treat
..................... 224
9.2.2
Imputation model
.................... 225
9.2.3
Inspecting imputations
.................. 227
9.2.4
Complete data analysis
................. 228
9.2.5
Results from the complete data analysis
........ 229
9.3
Time raster imputation
..................... 230
9.3.1
Change score
....................... 231
9.3.2
Scientific question: Critical periods
........... 232
9.3.3
Broken stick model
♦ .................. 234
9.3.4
Terneuzen Birth Cohort
................. 236
9.3.Õ
Shrinkage and the change score
*............ 237
9.3.6
Imputation
........................ 238
9.3.7
Complete data analysis
................. 240
9.4
Conclusion
............................ 242
9.5
Exercises
............................. 244
III Extensions
247
Contents xv
10
Conclusion
249
10.1
Some dangers, some do s and some don
t s...........
249
10.1.1
Some dangers
....................... 249
10.1.2
Some do s
......................... 250
10.1.3
Some don ts
........................ 251
10.2
Reporting
............................. 251
10.2.1
Reporting guidelines
................... 252
10.2.2
Template
......................... 254
10.3
Other applications
........................ 255
10.3.1
Synthetic
datasets
for data protection
......... 255
10.3.2
Imputation of potential outcomes
............ 255
10.3.3
Analysis of coarsened data
............... 256
10.3.4
File matching of multiple
datasets
........... 256
10.3.5
Planned missing data for efficient designs
....... 256
10.3.6
Adjusting for verification bias
.............. 257
10.3.7
Correcting for measurement error
............ 257
10.4
Future developments
...................... 257
10.4.1
Derived variables
..................... 257
10.4.2
Convergence of MICE algorithm
............ 257
10.4.3
Algorithms for blocks and batches
........... 258
10.4.4
Parallel computation
................... 258
10.4.5
Nested imputation
.................... 258
10.4.6
Machine learning for imputation
............ 259
10.4.7
Incorporating expert knowledge
............. 259
10.4.8
Distribution-free pooling rules
.............. 259
10.4.9
Improved diagnostic techniques
............. 260
10.4.10
Building block in modular statistics
..........
2(i()
10.5
Exercises
............................. 260
A Software
263
A.I
R
................................. 263
A.
2
S-PLUS
.............................. 265
A.3
Stata
............................... 265
A.4
SAS................................
266
A.5 SPSS
............................... 266
A.
6
Other software
.......................... 266
References
269
Author Index
299
Subject Index
307
|
| any_adam_object | 1 |
| author | Buuren, Stef van |
| author_GND | (DE-588)1070113697 |
| author_facet | Buuren, Stef van |
| author_role | aut |
| author_sort | Buuren, Stef van |
| author_variant | s v b sv svb |
| building | Verbundindex |
| bvnumber | BV039818462 |
| classification_rvk | MR 2100 QH 235 |
| ctrlnum | (OCoLC)775100172 (DE-599)BVBBV039818462 |
| discipline | Soziologie Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV039818462 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:12:08Z |
| institution | BVB |
| isbn | 9781439868249 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024678630 |
| oclc_num | 775100172 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-83 DE-20 DE-91 DE-BY-TUM DE-Er8 |
| owner_facet | DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-83 DE-20 DE-91 DE-BY-TUM DE-Er8 |
| physical | XXV, 316 S. graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Chapman & Hall, CRC interdisciplinary statistics series A Chapman & Hall book |
| spelling | Buuren, Stef van Verfasser (DE-588)1070113697 aut Flexible imputation of missing data Stef van Buuren Boca Raton [u.a.] CRC Press 2012 XXV, 316 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall, CRC interdisciplinary statistics series A Chapman & Hall book Imputationstechnik (DE-588)4609617-6 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Zurechnung (DE-588)4068129-4 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Fehlende Daten (DE-588)4264715-0 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 s R Programm (DE-588)4705956-4 s Fehlende Daten (DE-588)4264715-0 s Zurechnung (DE-588)4068129-4 s DE-604 Imputationstechnik (DE-588)4609617-6 s 1\p DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024678630&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Buuren, Stef van Flexible imputation of missing data Imputationstechnik (DE-588)4609617-6 gnd Multivariate Analyse (DE-588)4040708-1 gnd Zurechnung (DE-588)4068129-4 gnd R Programm (DE-588)4705956-4 gnd Fehlende Daten (DE-588)4264715-0 gnd |
| subject_GND | (DE-588)4609617-6 (DE-588)4040708-1 (DE-588)4068129-4 (DE-588)4705956-4 (DE-588)4264715-0 |
| title | Flexible imputation of missing data |
| title_auth | Flexible imputation of missing data |
| title_exact_search | Flexible imputation of missing data |
| title_full | Flexible imputation of missing data Stef van Buuren |
| title_fullStr | Flexible imputation of missing data Stef van Buuren |
| title_full_unstemmed | Flexible imputation of missing data Stef van Buuren |
| title_short | Flexible imputation of missing data |
| title_sort | flexible imputation of missing data |
| topic | Imputationstechnik (DE-588)4609617-6 gnd Multivariate Analyse (DE-588)4040708-1 gnd Zurechnung (DE-588)4068129-4 gnd R Programm (DE-588)4705956-4 gnd Fehlende Daten (DE-588)4264715-0 gnd |
| topic_facet | Imputationstechnik Multivariate Analyse Zurechnung R Programm Fehlende Daten |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024678630&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT buurenstefvan flexibleimputationofmissingdata |