Missing data

著者

• Allison, Paul David

書誌事項

Missing data

Paul D. Allison

（Sage university papers series, . Quantitative applications in the social sciences ; no. 07-136）

Sage Publications, c2002

注記

Includes bibliographical references (p. 89-91)

内容説明・目次

内容説明

Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.

目次

Series Editor's Introduction 1. Introduction 2. Assumptions Missing Completely at Random Missing at Random Ignorable Nonignorable 3. Conventional Methods Listwise Deletion Pairwise Deletion Dummy Variable Adjustment Imputation Summary 4. Maximum Likelihood Review of Maximum Likelihood ML With Missing Data Contingency Table Data Linear Models With Normally Distributed Data The EM Algorithm EM Example Direct ML Direct ML Example Conclusion 5. Multiple Imputation: Bascis Single Random Imputation Multiple Random Imputation Allowing for Random Variation in the Parameter Estimates Multiple Imputation Under the Multivariate Normal Model Data Augmentation for the Multivariate Normal Model Convergence in Data Augmentation Sequential Verses Parallel Chains of Data Augmentation Using the Normal Model for Nonnormal or Categorical Data Exploratory Analysis MI Example 1 6. Multiple Imputation: Complications Interactions and Nonlinearities in MI Compatibility of the Imputation Model and the Analysis Model Role of the Dependent Variable in Imputation Using Additional Variables in the Imputation Process Other Parametric Approaches to Multiple Imputation Nonparametric and Partially Parametric Methods Sequential Generalized Regression Models Linear Hypothesis Tests and Likelihood Ratio Tests MI Example 2 MI for Longitudinal and Other Clustered Data MI Example 3 7. Nonignorable Missing Data Two Classes of Models Heckman's Model for Sample Selection Bias ML Estimation With Pattern-Mixture Models Multiple Imputation With Pattern-Mixture Models 8. Summary and Conclusion Notes References About the Author

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BA53491196
• ISBN:
  • 9780761916727
• LCCN: 2001001295
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Thousand Oaks
• ページ数/冊数: vi, 93 p.
• 大きさ: 22 cm
• 分類:
  • LCC : QA276
• 件名:
  • LCSH : Mathematical statistics
  • LCSH : Missing observations (Statistics)
• 親書誌ID:
  • BA00656709