EMPIRICAL LIKELIHOOD METHODS FOR COMPLEX SURVEYS WITH DATA MISSING-BY-DESIGN
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DOI: 10.5705/ss.202016.0291
Abstract: We consider nonrandomized pretest-posttest designs with complex survey data for observational studies. We show that two-sample pseudo empirical likelihood methods provide efficient inferences on the treatment effect, with a missing-by-design feature used for forming the… read more here.
Keywords: likelihood; empirical likelihood; likelihood methods; missing design ... See more keywords
Classification and regression trees and forests for incomplete data from sample surveys
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DOI: 10.5705/ss.202017.0225
Abstract: Analysis of sample survey data often requires adjustments for missing values in the variables of interest. Standard adjustments based on item imputation or on propensity weighting factors rely on the availability of auxiliary variables for… read more here.
Keywords: classification regression; regression trees; trees forests; likelihood methods ... See more keywords