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• catalog abstract ""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."--pub. desc.".
• catalog contributor b12241358.
• catalog created "c2002.".
• catalog date "2002".
• catalog date "c2002.".
• catalog dateCopyrighted "c2002.".
• catalog description ""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."--pub. desc.".
• catalog description "1. Introduction -- 2. Assumptions ; Missing Completely at Random ; Missing at Random ; Ignorable ; Nonignorable -- 3. Conventional Methods ; Listwise ; Deletion; Pairwise Deletion ; Dummy Variable Adjustment ; Imputation -- 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 -- 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 -- 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 -- 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.".
• catalog description "Includes bibliographical references (p. 89-91) and index.".
• catalog extent "vi, 93 p. :".
• catalog identifier "0761916725 (pbk. : acid-free paper)".
• catalog isPartOf "Quantitative applications in the social sciences ; no. 07-136.".
• catalog isPartOf "Sage university papers. Quantitative applications in the social sciences ; no. 07-136".
• catalog issued "2002".
• catalog issued "c2002.".
• catalog language "eng".
• catalog publisher "Thousand Oaks, Calif. : Sage Publications,".
• catalog subject "001.4/22 21".
• catalog subject "Mathematical statistics.".
• catalog subject "Missing observations (Statistics)".
• catalog subject "QA276 .A55 2002".
• catalog tableOfContents "1. Introduction -- 2. Assumptions ; Missing Completely at Random ; Missing at Random ; Ignorable ; Nonignorable -- 3. Conventional Methods ; Listwise ; Deletion; Pairwise Deletion ; Dummy Variable Adjustment ; Imputation -- 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 -- 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 -- 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 -- 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.".
• catalog title "Missing data / Paul D. Allison.".
• catalog type "text".