Algorithms for constructing combined strata variance estimators

• 成果类型: Article
• 署名作者: Lu, Wilson W.; Brick, J. Michael; Sitter, Randy R.
• 署名单位: Westat; Simon Fraser University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214506000000267
• 发表日期: 2006
• 页码: 1680-1692
• 关键词: partially balanced designs; sample replication method; stratified samples; inference

摘要

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 efficient and can be adapted to easily account for vector characteristics and analytic domains. An important concern with combining strata is that the resulting variance estimators may be inconsistent. We establish conditions for the consistency of the combined variance estimator and show that the proposed algorithms ensure they are met. We also derive bounds on the degrees of freedom that the algorithms will assure. The algorithms are applied both to a real sample survey and to samples from simulated populations, and the algorithms perform very well, attaining variance estimators with precision levels close to the upper bounds.