Model-based Properties of Replication Variance Estimators for Sample Surveys

样本调查复制方差估计器的基于模型的属性

• 批准号: 0416662
• 负责人: Richard Valliant
• 金额: --
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0416662&HistoricalAwards=false
• 关键词: Model; based; Properties; Replication; Variance

Replication variance estimation in surveys of finite populations is a standard tool of survey statisticians and researchers. Two of the most common methods used are the jackknife and balanced repeated replication (BRR). There is a substantial amount of theory available for the replication methods when they are implemented in standard ways described in textbooks and journal articles. In practice, however, these methods are operationalized in ways that often do not fit the standard theoretical requirements. In the jackknife, for example, the basic approach is to delete one first-stage sample unit, compute an estimate based on the remaining sample units, cycle through all first-stage sample units, and compute a variance among the resulting set of estimates. In practice, groups of units are formed by combining units within or across strata. Entire groups are then dropped-out in order to compute a jackknife variance estimate. This project will evaluate methods used in practice and investigate potential improvements to current methods using the model-based approach to finite population sampling. In particular, this work will study weight adjustments in the grouped jackknife and, to a lesser extent, partially balanced BRR and model-based properties of these two methods of variance estimation. The general regression estimator of population totals and other nonlinear estimators will be emphasized.Theory for these grouped methods is limited, and it is unclear that the methods used in practice always have good theoretical properties. The ramifications of poor implementation of replicate variance estimation can be important because of the way that data bases are constructed. Weights for the full sample and for subsamples (or replicates) are created by the database constructor who appends the weights to each record in the database. Users are then instructed to use those weights to compute variances for all statistics, regardless of how complex. If a poor set of replicate weights is created, this affects all analysts. The research is intended to provide guidance on how to implement these grouped methods in surveys, particularly ones concerning the economy, health status of the population, and other applications in the social sciences. This research is supported by the Methodology, Measurement, and Statistics Program, the Statistics and Probability Program, and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.

有限总体调查中的复制方差估计是调查统计学家和研究人员的标准工具。最常用的两种方法是折叠法和平衡重复复制(BRR)。当复制方法以教科书和期刊文章中描述的标准方式实施时，有大量的理论可用。然而，在实践中，这些方法的可操作性往往不符合标准的理论要求。例如，在折叠式方法中，基本方法是删除一个第一阶段样本单元，根据剩余的样本单元计算估计，循环遍历所有第一阶段样本单元，并计算得到的一组估计中的方差。在实践中，单元组是由层内或层间的单元组合而成的。然后，为了计算折叠式方差估计，整个组都会被淘汰。该项目将评估实践中使用的方法，并使用有限总体抽样的基于模型的方法来调查对当前方法的潜在改进。特别是，这项工作将研究组合折刀中的权重调整，以及在较小程度上部分平衡BRR和这两种方差估计方法的基于模型的性质。重点讨论总体总数的一般回归估计量和其他非线性估计量。这些分组方法的理论是有限的，并且不清楚实际使用的方法是否总是具有良好的理论性质。由于数据库的构建方式，重复方差估计执行不力的后果可能很重要。完整样本和子样本(或复制)的权重由数据库构造函数创建，该构造函数将权重附加到数据库中的每个记录。然后，指示用户使用这些权重来计算所有统计数据的方差，无论这些统计数据有多复杂。如果创建了一组糟糕的复制权重，这将影响所有分析师。这项研究旨在就如何在调查中实施这些分组方法提供指导，特别是关于经济、人口健康状况和社会科学其他应用的调查。这项研究得到了方法学、测量和统计计划、统计和概率计划以及联邦统计机构联盟的支持，这是支持调查和统计方法研究的联合活动的一部分。