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November 1, 1988Technometrics

Statistical Analysis With Missing Data

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Authors

SGSubir GhoshRajshahi University of Engineering and Technology

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Implication

Analyzes methods for addressing missing data issues in statistical analysis, suggesting better practices.

Key Points

  • The aim is to explore various methodologies for handling missing data in statistical analyses.
  • Review of statistical techniques for managing missing data
  • Assessment of data imputation methods
  • Evaluation of bias and variance impacts due to missing entries.
  • Identified key strategies to minimize bias when dealing with missing data
  • Highlighted the importance of appropriate imputation methods
  • Demonstrated that some methods significantly lower variance in analysis outcomes.

Cite This Study

Subir Ghosh (1988) studied this question.

synapsesocial.com/papers/6a11ceec17704c0cccdce474https://doi.org/10.1080/00401706.1988.10488446
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Also Consider

Synapse has enriched one closely related paper. Consider it for comparative context:

  1. 1Analysis of Experiments with Missing Data1987 · 54 citations