Missing data: our view of the state of the art.
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Summary
2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
- Type
- article
- Published
- 2002-05-30
- Cited by
- 11,907
- References
- 110
- Access
- Open access
- OpenAlex
- https://openalex.org/W2156267802
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7745507
Keywords
Missing data, Imputation (statistics), Computer science, Bayesian probability, Mainstream
References
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- Multiple-Imputation Inferences with Uncongenial Sources of Input
- Research methods in psychology
- Finite Mixture Models
- The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models
- The science of prevention: methodological advances from alcohol and substance abuse research.
- BMPD statistical software manual
- Probability and Statistical Inference
- Development, Implementation and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets
- Flexible mutlivariate imputation by MICE
- More on EM for ML factor analysis
- A comparison of inclusive and restrictive strategies in modern missing data procedures.
- Models for longitudinal data: a generalized estimating equation approach.
- Maximizing the Usefulness of Data Obtained with Planned Missing Value Patterns: An Application of Maximum Likelihood Procedures.
- Inference with Imputed Conditional Means
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- A stakeholder process evaluation of a social marketing walking intervention targeting children
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- Two-pronged Strategy for Using DIC to Compare Selection Models with Non-Ignorable Missing Responses
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