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Bibliographic Details
Main Author:	Allison, Paul D. (Author)
Format:	Book
Language:	English
Published:	Thousand Oaks, Calif. [u.a.] Sage 2009
Edition:	[Nachdr.]
Series:	Quantitative applications in the social sciences 136
Subjects:	Fehlende Daten Imputationstechnik Statistik
Online Access:	Inhaltsverzeichnis
Physical Description:	VI, 93 S. graph. Darst.
ISBN:	0761916725

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adam_text	Titel: Missing data Autor: Allison, Paul David Jahr: 2009 CONTENTS Series Editor s Introduction í 1. Introduction 1 2. Assumptions 3 Missing Completely at Random 3 Missing at Random 4 Ignorable 5 Nonignorable 5 3. Conventional Methods 5 Listwise Deletion 6 Pairwise Deletion 8 Dummy Variable Adjustment 9 Imputation 11 Summary 12 4. Maximum Likelihood 12 Review of Maximum Likelihood 13 ML With Missing Data 14 Contingency Table Data 15 Linear Models With Normally Distributed Data 18 The EM Algorithm 19 EM Example 21 Direct ML 23 Direct ML Example 25 Conclusion 26 5. Multiple Imputation: Basics 27 Single Random Imputation 28 Multiple Random Imputation 29 Allowing for Random Variation in the Parameter Estimates 30 Multiple Imputation Under the Multivariate Normal Model 32 Data Augmentation for the Multivariate Normal Model 34 Convergence in Data Augmentation 36 Sequential Versus Parallel Chains of Data Augmentation 37 Using the Normal Model for Nonnormal or Categorical Data 3° Exploratory Analysis 41 MI Example 1 41 6. Multiple Imputation: Complications ™ Interactions and Nonlinearities in MI 50 Compatibility of the Imputation Model and the Analysis Model 52 Role of the Dependent Variable in Imputation 53 Using Additional Variables in the Imputation Process 54 Other Parametric Approaches to Multiple Imputation 55 Nonparametric and Partially Parametric Methods 57 Sequential Generalized Regression Models 64 Linear Hypothesis Tests and Likelihood Ratio Tests 65 MI Example 2 68 MI for Longitudinal and Other Clustered Data 73 MI Example 3 74 7. Nonignorable Missing Data ^7 Two Classes of Models 78 Heckman s Model for Sample Selection Bias 79 ML Estimation With Pattern-Mixture Models 82 Multiple Imputation With Pattern-Mixture Models 83 8. Summary and Conclusion 84 Notes 87 References 89 About the Author 93
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illustrated	Illustrated
indexdate	2025-02-20T06:50:33Z
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isbn	0761916725
language	English
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physical	VI, 93 S. graph. Darst.
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publishDateSort	2009
publisher	Sage
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series	Quantitative applications in the social sciences
series2	Quantitative applications in the social sciences
spellingShingle	Allison, Paul D. Missing data Quantitative applications in the social sciences Fehlende Daten (DE-588)4264715-0 gnd Imputationstechnik (DE-588)4609617-6 gnd Statistik (DE-588)4056995-0 gnd
subject_GND	(DE-588)4264715-0 (DE-588)4609617-6 (DE-588)4056995-0
title	Missing data
title_auth	Missing data
title_exact_search	Missing data
title_full	Missing data Paul D. Allison
title_fullStr	Missing data Paul D. Allison
title_full_unstemmed	Missing data Paul D. Allison
title_short	Missing data
title_sort	missing data
topic	Fehlende Daten (DE-588)4264715-0 gnd Imputationstechnik (DE-588)4609617-6 gnd Statistik (DE-588)4056995-0 gnd
topic_facet	Fehlende Daten Imputationstechnik Statistik
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work_keys_str_mv	AT allisonpauld missingdata

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