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Jackknife empirical likelihood method for multiply robust estimation with missing data

Sixia Chen and David Haziza

Computational Statistics & Data Analysis, 2018, vol. 127, issue C, 258-268

Abstract: A novel jackknife empirical likelihood method for constructing confidence intervals for multiply robust estimators is proposed in the context of missing data. Under mild regularity conditions, the proposed jackknife empirical likelihood ratio has been shown to converge to a standard chi-square distribution. A simulation study supports the findings and shows the benefits of the proposed method. The latter has also been applied to 2016 National Health Interview Survey data.

Keywords: Double robustness; Imputation; Nonresponse model (search for similar items in EconPapers)
Date: 2018
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