A unified framework of multiply robust estimation approaches for handling incomplete data

Sixia Chen and David Haziza

Computational Statistics & Data Analysis, 2023, vol. 179, issue C

Abstract: Missing data occur frequently in practice. Inverse probability weighting and imputation are regarded as two important approaches for handling missing data. However, the validity of these approaches depends on underlying model assumptions. A new general framework for multiply robust estimation procedures by combining multiple nonresponse and imputation models is proposed in the paper. The proposed method can be used to estimate both smooth and non-smooth parameters defined as the solution of some estimating equations. It includes population means, quantiles, and distribution functions as special cases. The asymptotic results of the proposed methods are established. The results of a simulation study and a real data application suggest that the proposed methods perform well in terms of bias and efficiency.

Keywords: Estimating equations; Fractional imputation; Outcome regression model; Propensity score estimation; Variance estimation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:179:y:2023:i:c:s0167947322002262

DOI: 10.1016/j.csda.2022.107646

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