Statistical analysis with missing data:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, NJ
Wiley
2020
|
| Edition: | 3rd edition |
| Series: | Wiley series in probability and statistics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xii, 449 Seiten Diagramme |
| ISBN: | 9780470526798 |
Staff View
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Record in the Search Index
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Contents Preface to the Third Edition Part I xi Overview and Basic Approaches 1 1 Introduction 1.1 1.2 1.3 1.4 The Problem of Missing Data 3 Missingness Patterns and Mechanisms 8 Mechanisms That Lead to Missing Data 13 A Taxonomy of Missing Data Methods 23 2 2.1 2.2 2.3 2.4 2.4.1 2.4.2 2.4.3 2.4.4 2.5 2.5.1 2.5.2 2.5.3 2.5.4 Missing Data in Experiments 29 Introduction 29 The Exact Least Squares Solution with Complete Data 30 The Correct Least Squares Analysis with Missing Data 32 Filling in Least Squares Estimates 33 Yates’s Method 33 Using a Formula for the Missing Values 34 Iterating to Find the Missing Values 34 ANCOVA with Missing Value Covariates 35 Bartlett’s ANCOVA Method 35 Useful Properties of Bartlett’s Method 35 Notation 36 The ANCOVA Estimates of Parameters and Missing Y- Values 36 ANCOVA Estimates of the Residual Sums of Squares and the Covariance Matrix of ß 37 Least Squares Estimates of Missing Values by ANCOVA Using Only Complete-Data Methods 38 Correct Least Squares Estimates of Standard Errors and One Degree of Freedom Sums of Squares 40 2.6 2.7 3
vi ļ Contents 2.8 Correct Least-Squares Sums of Squares with More Than One Degree of Freedom 42 3 Complete-Case and Available-Case Analysis, Including Weighting Methods 47 3.1 3.2 3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.4 Introduction 47 Complete-Case Analysis 47 Weighted Complete-Case Analysis SO Weighting Adjustments SO Poststratification and Raking to Known Margins Inference from Weighted Data 60 Summary of Weighting Methods 61 Available-Case Analysis 61 4 Single Imputation Methods 67 Introduction 67 Imputing Means from a Predictive Distribution 69 Unconditional Mean Imputation 69 Conditional Mean Imputation 70 Imputing Draws from a Predictive Distribution 73 Draws Based on Explicit Models 73 Draws Based on Implicit Models - Hot Deck Methods Conclusion 81 4.1 4.2 4.2.1 4.2.2 4.3 4.3.1 4.3.2 4.4 5 5.1 5.2 5.3 5.3.1 5.3.2 5.4 5.5 6.1 6.1.1 6.1.2 6.1.3 76 Accounting for Uncertainty from Missing Data 85 Introduction 85 Imputation Methods that Provide Valid Standard Errors from a Single Filled-in Data Set 86 Standard Errors for Imputed Data by Resampling 90 Bootstrap Standard Errors 90 Jackknife Standard Errors 92 Introduction to Multiple Imputation 95 Comparison of Resampling Methods and Multiple Imputation Part II 6 58 Likelihood-Based Approaches to the Analysis of Data with Missing Values 107 109 Review of Likelihood-Based Estimation for Complete Data 109 Maximum Likelihood Estimation 109 Inference Based on the Likelihood 118 Large Sample Maximum Likelihood and Bayes Inference 119 Theory of Inference Based on the Likelihood Function 100
Contents 6.1.4 6.1.5 6.2 6.3 6.3.1 6.3.2 6.3.3 6.4 7 7.1 7.2 7.2.1 7.2.2 7.3 7.4 7.4.1 7.4.2 7.4.3 7.4.4 7.5 Bayes Inference Based on the Full Posterior Distribution 126 Simulating Posterior Distributions 130 Likelihood-Based Inference with Incomplete Data 132 A Generally Flawed Alternative to Maximum Likelihood: Maximizing over the Parameters and the Missing Data 141 The Method 141 Background 142 Examples 143 Likelihood Theory for Coarsened Data 145 Factored Likelihood Methods When the Missingness Mechanism Is Ignorable 151 Introduction 151 Bivariate Normal Data with One Variable Subject to Missingness: ML Estimation 153 ML Estimates 153 Large-Sample Covariance Matrix 157 Bivariate Normal Monotone Data: Small-Sample Inference 158 Monotone Missingness with More Than Two Variables 161 Multivariate Data with One Normal Variable Subject to Missingness 161 The Factored Likelihood for a General Monotone Pattern 162 ML Computation for Monotone Normal Data via the Sweep Operator 166 Bayes Computation for Monotone Normal Data via the Sweep Operator 174 Factored Likelihoods for Special Nonmonotone Patterns 175 8 Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse 185 8.1 8.2 8.3 8.4 8.4.1 8.4.2 8.4.3 8.5 8.5.1 8.5.2 8.5.3 8.6 Alternative Computational Strategies 185 Introduction to the EM Algorithm 187 The E Step and The M Step of EM 188 Theory of the EM Algorithm 193 Convergence Properties of EM 193 EM for Exponential Families 196 Rate of Convergence of EM 198 Extensions of EM 200 The ECM Algorithm 200 The ECME and AECM
Algorithms 205 The PX-EM Algorithm 206 Hybrid Maximization Methods 208 vii
viii Contents 9 Large-Sample Inference Based on Maximum Likelihood Estimates 213 9.1 9.2 9.2.1 9.2.2 9.2.3 9.2.4 Standard Errors Based on The Information Matrix 213 Standard Errors via Other Methods 214 The Supplemented EM Algorithm 214 Bootstrapping the Observed Data 219 Other Large-Sample Methods 220 Posterior Standard Errors from Bayesian Methods 221 10 Bayes and Multiple Imputation 223 10.1 Bayesian Iterative Simulation Methods 223 10.1.1 Data Augmentation 223 10.1.2 The Gibbs’ Sampler 226 10.1.3 Assessing Convergence of Iterative Simulations 230 10.1.4 Some Other Simulation Methods 231 10.2 Multiple Imputation 232 10.2.1 Large-Sample Bayesian Approximations of the Posterior Mean and Variance Based on a Small Number of Draws 232 10.2.2 Approximations Using Test Statistics or ¿»-Values 235 10.2.3 Other Methods for Creating Multiple Imputations 238 10.2.4 Chained-Equation Multiple Imputation 241 10.2.5 Using Different Models for Imputation and Analysis 243 Part III Likelihood-Based Approaches to the Analysis of Incomplete Data: Some Examples 247 11 Multivariate Normal Examples, Ignoring the Missingness Mechanism 249 11.1 11.2 Introduction 249 Inference for a Mean Vector and Covariance Matrix with Missing Data Under Normality 249 The EM Algorithm for Incomplete Multivariate Normal Samples 250 Estimated Asymptotic Covariance Matrix of (Θ - Θ) 252 Bayes Inference and Multiple Imputation for the Normal Model 253 The Normal Model with a Restricted Covariance Matrix 257 Multiple Linear Regression 264 Linear Regression with Missingness Confined to the Dependent Variable 264 More
General Linear Regression Problems with Missing Data 266 A General Repeated-Measures Model with Missing Data 269 11.2.1 11.2.2 11.2.3 11.3 11.4 11.4.1 11.4.2 11.5
11.6 11.6.1 11.6.2 11.6.3 11.7 12 12.1 12.2 12.2.1 12.2.2 12.2.3 12.2.4 12.2.5 12.2.6 12.3 Time Series Models 273 Introduction 273 Autoregressive Models for Univariate Time Serieswith Missing Values 273 Kalman Filter Models 276 Measurement Error Formulated as Missing Data 277 Models for Robust Estimation 285 Introduction 285 Reducing the Influence of Outliers by Replacing the Normal Distribution by a Longer-Tailed Distribution 286 Estimation for a Univariate Sample 286 Robust Estimation of the Mean and Covariance Matrix with Complete Data 288 Robust Estimation of the Mean and Covariance Matrix from Data with Missing Values 290 Adaptive Robust Multivariate Estimation 291 Bayes Inference for the t Model 292 Further Extensions of the t Model 294 Penalized Spline of Propensity Prediction 298 13 Models for Partially Classified Contingency Tables, Ignoring the Missingness Mechanism 301 13.1 13.2 13.2.1 13.2.2 13.2.3 13.3 Introduction 301 Factored Likelihoods for Monotone Multinomial Data 302 Introduction 302 ML and Bayes for Monotone Patterns 303 Precision of Estimation 312 ML and Bayes Estimation for Multinomial Samples with General Patterns of Missingness 313 Loglinear Models for Partially Classified Contingency Tables 317 The Complete-Data Case 317 Loglinear Models for Partially Classified Tables 320 Goodness-of-Fit Tests for Partially Classified Data 326 13.4 13.4.1 13.4.2 13.4.3 14 Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missingness Mechanism 329 14.1 14.2 14.2.1 14.2.2 14.2.3 Introduction 329 The General Location Model 329 The Complete-Data Model
and Parameter Estimates 329 ML Estimation with Missing Values 331 Details of the E Step Calculations 334
Contents 14,2.4 Bayes’ Computation for the Unrestricted General Location Model 335 14.3 The General Location Model with Parameter Constraints 337 14.3.1 Introduction 337 14.3.2 Restricted Models for the Cell Means 340 14.3.3 Loglinear Models for the Cell Probabilities 340 14.3.4 Modifications to the Algorithms of Previous Sections to Accommodate Parameter Restrictions 340 14.3.5 Simplifications When Categorical Variables are More Observed than Continuous Variables 343 14.4 Regression Problems Involving Mixtures of Continuous and Categorical Variables 344 14.4.1 Normal Linear Regression with Missing Continuous or Categorical Covariates 344 14.4.2 Logistic Regression with Missing Continuous or Categorical Covariates 346 14.5 Further Extensions of the General Location Model 347 15 15.1 15.2 15.3 15.3.1 15.3.2 15.3.3 15.3.4 15.3.5 15.3.6 15.4 15.4.1 15.4.2 15.4.3 15.4.4 351 Introduction 351 Models with Known MNAR Missingness Mechanisms: Grouped and Rounded Data 355 Normal Models for MNAR Missing Data 362 Normal Selection and Pattern-Mixture Models for Univariate Missingness 362 Following up a Subsample of Nonrespondents 364 The Bayesian Approach 366 Imposing Restrictions on Model Parameters 369 Sensitivity Analysis 376 Subsample Ignorable Likelihood for Regression with Missing Data 379 Other Models and Methods for MNAR Missing Data 382 MNAR Models for Repeated-Measures Data 382 MNAR Models for Categorical Data 385 Sensitivity Analyses for Chained-Equation Multiple Imputations 391 Sensitivity Analyses in Pharmaceutical Applications 396 Missing Not at Random Models References 405
Author Index 429 Subject Index 437 |
| any_adam_object | 1 |
| author | Little, Roderick J. A. 1949- Rubin, Donald B. 1943- |
| author_GND | (DE-588)138949581 (DE-588)131607618 |
| author_facet | Little, Roderick J. A. 1949- Rubin, Donald B. 1943- |
| author_role | aut aut |
| author_sort | Little, Roderick J. A. 1949- |
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| discipline | Wirtschaftswissenschaften |
| edition | 3rd edition |
| format | Book |
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| id | DE-604.BV040353868 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-13T13:01:56Z |
| institution | BVB |
| isbn | 9780470526798 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025207876 |
| oclc_num | 812213803 |
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| owner | DE-473 DE-BY-UBG DE-945 DE-11 DE-703 |
| owner_facet | DE-473 DE-BY-UBG DE-945 DE-11 DE-703 |
| physical | xii, 449 Seiten Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Little, Roderick J. A. 1949- Verfasser (DE-588)138949581 aut Statistical analysis with missing data Roderick J. A. Little (Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, and Research Professor, Institute for Social Research, at the University of Michigan), Donald B. Rubin (Professor at Yau Mathematical Sciences Center, Tsinghua University; Murray Shusterman Senior Research Fellow, Fox School of Business, at Temple University; and Professor Emeritus, at Harvard University) 3rd edition Hoboken, NJ Wiley 2020 xii, 449 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Ungewissheit (DE-588)4137198-7 gnd rswk-swf Element Statistik (DE-588)4448257-7 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Fehlende Daten (DE-588)4264715-0 gnd rswk-swf Datenauswertung (DE-588)4131193-0 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s Element Statistik (DE-588)4448257-7 s Statistische Analyse (DE-588)4116599-8 s Fehlende Daten (DE-588)4264715-0 s 1\p DE-604 Datenauswertung (DE-588)4131193-0 s 2\p DE-604 Multivariate Analyse (DE-588)4040708-1 s 3\p DE-604 Ungewissheit (DE-588)4137198-7 s 4\p DE-604 Rubin, Donald B. 1943- Verfasser (DE-588)131607618 aut Erscheint auch als Online-Ausgabe, PDF 978-1-118-59601-2 Erscheint auch als Online-Ausgabe, EPUB 978-1-118-59569-5 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=025207876&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Little, Roderick J. A. 1949- Rubin, Donald B. 1943- Statistical analysis with missing data Ungewissheit (DE-588)4137198-7 gnd Element Statistik (DE-588)4448257-7 gnd Statistische Analyse (DE-588)4116599-8 gnd Multivariate Analyse (DE-588)4040708-1 gnd Fehlende Daten (DE-588)4264715-0 gnd Datenauswertung (DE-588)4131193-0 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4137198-7 (DE-588)4448257-7 (DE-588)4116599-8 (DE-588)4040708-1 (DE-588)4264715-0 (DE-588)4131193-0 (DE-588)4056995-0 |
| title | Statistical analysis with missing data |
| title_auth | Statistical analysis with missing data |
| title_exact_search | Statistical analysis with missing data |
| title_full | Statistical analysis with missing data Roderick J. A. Little (Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, and Research Professor, Institute for Social Research, at the University of Michigan), Donald B. Rubin (Professor at Yau Mathematical Sciences Center, Tsinghua University; Murray Shusterman Senior Research Fellow, Fox School of Business, at Temple University; and Professor Emeritus, at Harvard University) |
| title_fullStr | Statistical analysis with missing data Roderick J. A. Little (Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, and Research Professor, Institute for Social Research, at the University of Michigan), Donald B. Rubin (Professor at Yau Mathematical Sciences Center, Tsinghua University; Murray Shusterman Senior Research Fellow, Fox School of Business, at Temple University; and Professor Emeritus, at Harvard University) |
| title_full_unstemmed | Statistical analysis with missing data Roderick J. A. Little (Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, and Research Professor, Institute for Social Research, at the University of Michigan), Donald B. Rubin (Professor at Yau Mathematical Sciences Center, Tsinghua University; Murray Shusterman Senior Research Fellow, Fox School of Business, at Temple University; and Professor Emeritus, at Harvard University) |
| title_short | Statistical analysis with missing data |
| title_sort | statistical analysis with missing data |
| topic | Ungewissheit (DE-588)4137198-7 gnd Element Statistik (DE-588)4448257-7 gnd Statistische Analyse (DE-588)4116599-8 gnd Multivariate Analyse (DE-588)4040708-1 gnd Fehlende Daten (DE-588)4264715-0 gnd Datenauswertung (DE-588)4131193-0 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Ungewissheit Element Statistik Statistische Analyse Multivariate Analyse Fehlende Daten Datenauswertung Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025207876&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT littleroderickja statisticalanalysiswithmissingdata AT rubindonaldb statisticalanalysiswithmissingdata |