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What is Review of Regression with Missing Data about?

This document reviews regression analysis methods for handling missing values in independent variables, categorizing them into six classes: complete case analysis, available case methods, least squares on imputed data, maximum likelihood, Bayesian methods, and multiple imputation. It emphasizes the importance of using likelihood-based methods over least squares methods when data is not missing completely at random and suggests the need for better software solutions. The paper also discusses the implications

Author

Joanna Chen

Language

EN