Missing data analysis in practice:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press
[2016]
|
| Series: | Chapman & Hall/CRC interdisciplinary statistics series
A Chapman and Hall book |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIX, 210 Seiten Diagramme. - Illustrationen, Diagramme |
| ISBN: | 9781482211924 |
Staff View
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Record in the Search Index
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| adam_text | Contents
List of Tables xiii
List of Figures xv
Preface xvii
1 Basic Concepts 1
1.1 Introduction ................................................. 1
1.2 Definition of Missing Values ................................. 2
1.3 Missing Data Pattern ..................................... 3
1.4 Missing Data Mechanism.................................... 4
1.5 Problems with Complete-Case Analysis . ....................... 7
1.6 Analysis Approaches........................................... 9
1.7 Basic Statistical Concepts................................... 13
1.8 A Chuckle or Two............................................. 19
1.9 Bibliographic Note........................................... 21
1.10 Exercises ................................................... 23
2 Weighting Methods 27
2.1 Motivation ................................................ 27
2.2 Adjustment Cell Method ...................................... 29
2.3 Response Propensity Model.................................... 29
2.4 Example ..................................................... 32
2.5 Impact of Weights on Population Mean Estimates .............. 37
2.6 Post-Stratification.......................................... 39
2.6.1 Post-Stratification Weights............................ 39
2.6.2 Raking................................................. 40
2.6.3 Post-stratified Estimator.............................. 42
2.7 Survey Weights .............................................. 44
2.8 Alternative to Weighted Analysis ............................ 45
IX
Contents
x
2.9 Inverse Probability Weighting ............................. 47
2.10 Bibliographic Note........................................... 47
T, 4Q
2.11 Exercises ................................................
3 Imputation ^
3.1 Generation of Plausible Values................................ 53
3.2 Hot Deck Imputation .......................................... 55
3.2.1 Connection with Weighting.............................. 57
3.2.2 Bayesian Modification.................................. 58
3.3 Model Based Imputation ...................................... 59
3.4 Example ...................................................... 63
3.5 Sequential Regression Imputation ............................. 67
3.5.1 Details................................................ 69
3.5.2 Handling Restrictions.................................. 71
3.5.3 Model Fitting Issues................................... 73
3.6 Bibliographic Note............................................ 75
3.7 Exercises .................................................... 76
4 Multiple Imputation 77
4.1 Introduction ................................................. 77
4.2 Basic Combining Rule.......................................... 77
4.3 Multivariate Hypothesis Testing .............................. 79
4.4 Combining Test Statistics..................................... 80
4.5 Basic Theory of Multiple Imputation .......................... 82
4.6 Extended Combining Rules...................................... 83
4.6.1 Transformation......................................... 84
4.6.2 Nonnormal Approximation................................ 85
4.7 Some Practical Issues ........................................ 86
4.7.1 Number of Imputations.................................. 86
4.7.2 Diagnostics............................................ 87
4.7.3 To Impute or Not to Impute ............................ 88
4.8 Revisiting Examples........................................... 89
4.8.1 Data in Table 1.1...................................... 89
4.8.2 Case-Control Study .................................... 90
4.9 Example: St. Louis Risk Research Project ..................... 91
4.10 Bibliographic Note........................................... 95
4.11 Exercises ................................................... 95
Contents
xi
5 Regression Analysis 99
5.1 General Observations ..................................... 99
5.1.1 Imputation Issues .................................. 99
5.2 Revisiting St. Louis Risk Research Example .............. 103
5.3 Analysis of Variance..................................... 105
5.3.1 Complete Data Analysis............................. 106
5.3.1.1 Partitioning of Sum of Squares............ 106
5.3.1.2 Regression Formulation.................... 108
5.3.2 ANOVA with Missing Values.......................... 108
5.3.2.1 Combining Sums of Squares ................ 109
5.3.2.2 Regression Formulation with Missing Values 110
5.3.3 Example............................................ 110
5.3.4 Extensions ...................................... 112
5.4 Survival Analysis Example ............................... 113
5.5 Bibliographic Note....................................... 117
5.6 Exercises ............................................... 117
6 Longitudinal Analysis with Missing Values 121
6.1 Introduction ............................................ 121
6.2 Imputation Model Assumption.............................. 124
6.2.1 Completed as Randomized ........................... 126
6.2.2 Completed as Control............................... 128
6.2.3 Completed as Stable................................ 129
6.3 Example ................................................. 130
6.3.1 Completed as Randomized: Maximum Likelihood Anal-
ysis ..................................................... 130
6.3.2 Multiple Imputation: Completed as Randomized . . . 133
6.3.3 Multiple Imputation: Completed as Control..... 135
6.4 Practical Issues ........................................ 135
6.5 Weighting Methods ....................................... 136
6.6 Binary Example .......................................... 139
6.7 Bibliographic Note....................................... 142
6.8 Exercises ............................................... 143
7 Nonignorable Missing Data Mechanisms 145
7.1 Modeling Framework ...................................... 143
7.2 EM-Algorithm ............................................ 146
Contents
7.3 Inference under Selection Model ............................ 148
7.4 Inference under Mixture Model............................... 151
7.5 Example .................................................... 151
7.6 Practical Considerations ................................... 152
7.7 Bibliographic Note.......................................... 153
7.8 Exercises .................................................. 154
8 Other Applications 155
8.1 Measurement Error .......................................... 155
8.2 Combining Information from Multiple Data Sources............ 159
8.3 Bayesian Inference from Finite Population .................. 160
8.4 Causal Inference............................................ 163
8.5 Disclosure Limitation ...................................... 165
8.6 Bibliographic Note.......................................... 169
8.7 Problems.................................................... 170
9 Other Topics 175
9.1 Uncongeniality and Multiple Imputation ..................... 175
9.2 Multiple Imputation for Complex Surveys..................... 177
9.3 Missing Values by Design.................................... 179
9.4 Replication Method for Variance Estimation ................. 180
9.5 Final Thoughts.............................................. 182
9.6 Bibliographic Note.......................................... 183
9.7 Exercises .................................................. 184
Bibliography 187
Index
205
|
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| classification_rvk | SK 850 QH 235 |
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| discipline | Mathematik Wirtschaftswissenschaften |
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| id | DE-604.BV043575155 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:29:14Z |
| institution | BVB |
| isbn | 9781482211924 |
| language | English |
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| physical | XIX, 210 Seiten Diagramme. - Illustrationen, Diagramme |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
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| record_format | marc |
| series2 | Chapman & Hall/CRC interdisciplinary statistics series A Chapman and Hall book |
| spelling | Raghunathan, Trivellore Verfasser (DE-588)1079934650 aut Missing data analysis in practice Trivellore Raghunathan, University of Michigan Ann Arbor, Michigan USA Boca Raton ; London ; New York CRC Press [2016] © 2016 XIX, 210 Seiten Diagramme. - Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC interdisciplinary statistics series A Chapman and Hall book Missing observations (Statistics) Anwendung (DE-588)4196864-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Fehlende Daten (DE-588)4264715-0 gnd rswk-swf Statistik (DE-588)4056995-0 s Anwendung (DE-588)4196864-5 s Datenanalyse (DE-588)4123037-1 s Fehlende Daten (DE-588)4264715-0 s b DE-604 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028989921&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Raghunathan, Trivellore Missing data analysis in practice Missing observations (Statistics) Anwendung (DE-588)4196864-5 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd Fehlende Daten (DE-588)4264715-0 gnd |
| subject_GND | (DE-588)4196864-5 (DE-588)4123037-1 (DE-588)4056995-0 (DE-588)4264715-0 |
| title | Missing data analysis in practice |
| title_auth | Missing data analysis in practice |
| title_exact_search | Missing data analysis in practice |
| title_full | Missing data analysis in practice Trivellore Raghunathan, University of Michigan Ann Arbor, Michigan USA |
| title_fullStr | Missing data analysis in practice Trivellore Raghunathan, University of Michigan Ann Arbor, Michigan USA |
| title_full_unstemmed | Missing data analysis in practice Trivellore Raghunathan, University of Michigan Ann Arbor, Michigan USA |
| title_short | Missing data analysis in practice |
| title_sort | missing data analysis in practice |
| topic | Missing observations (Statistics) Anwendung (DE-588)4196864-5 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd Fehlende Daten (DE-588)4264715-0 gnd |
| topic_facet | Missing observations (Statistics) Anwendung Datenanalyse Statistik Fehlende Daten |
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