Missing observations due to non-response are commonly encountered in data collected from sample surveys. The focus of this article is on item non-response which is often handled by filling in (or imputing) missing values using the observed responses (donors). Random imputation (single or fractional) is used within homogeneous imputation classes that are formed on the basis of categorical auxiliary variables observed on all the sampled units. A uniform response rate within classes is assumed, but that rate is allowed to vary across classes. We construct confidence intervals (CIs) for a population parameter that is defined as the solution to a smooth estimating equation with data collected using stratified simple random sampling. The imputation classes are assumed to be formed across strata. Fractional imputation with a fixed number of random draws is used to obtain an imputed estimating function. An empirical likelihood inference method under the fractional imputation is proposed and its asymptotic properties are derived. Two asymptotically correct bootstrap methods are developed for constructing the desired CIs. In a simulation study, the proposed bootstrap methods are shown to outperform traditional bootstrap methods and some non-bootstrap competitors under various simulation settings. The Canadian Journal of Statistics 47: 281–301; 2019

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Canadian Journal of Statistics
School of Mathematics and Statistics

Cai, S, Qin, Y. (Yongsong), Rao, J.N.K, & Winiszewska, M. (Malgorzata). (2019). Empirical likelihood confidence intervals under imputation for missing survey data from stratified simple random sampling. Canadian Journal of Statistics, 47(2), 281–301. doi:10.1002/cjs.11493