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构造合并分层方差估计量的算法AI 解读

A jackknife or balanced repeated replication variance estimator in a large survey typically requires a large number of replicates and replicate weights. Reducing the number of replicates may have important advantages for computations and for limiting the risk of data disclosure from public use data files. This article proposes algorithms adapted from scheduling theory to combine variance strata and, thus, reduce the number of replicates. The algorithms are simple and…

调查抽样中多重插补方差估计量的偏差AI 解读

Summary Multiple imputation is a method of estimating the variances of estimators that are constructed with some imputed data. We give an expression for the bias of the multiple-imputation variance estimator for data that are collected with a complex sample design. The bias may be sizable for certain estimators, such as domain means, when a large fraction of the values are imputed. A bias-adjusted variance estimator is suggested.