论文标题
在两相设计中用于回归建模的最佳多波抽样
Optimal multi-wave sampling for regression modelling in two-phase designs
论文作者
论文摘要
两阶段的设计涉及测量已经测量一些变量的队列子集上的额外变量。两阶段设计的目的是从队列中选择一个个体的子样本，并有效地分析该子样本。获得最佳设计，从而提供最有效的回归参数估计值。在本文中，我们提出了一种多波抽样设计，以近似基于设计的估计器的最佳设计。影响功能用于计算最佳抽样分配。我们建议在回归参数上使用信息的先验来得出WOVE-1采样概率，因为任何预先指定的采样概率可能远非最佳和降低效率。统计分析中使用了广义耙。我们表明，具有合理信息的先验的两波抽样最终将获得更高的关注参数的精度，并接近基础最佳设计。
Two-phase designs involve measuring extra variables on a subset of the cohort where some variables are already measured. The goal of two-phase designs is to choose a subsample of individuals from the cohort and analyse that subsample efficiently. It is of interest to obtain an optimal design that gives the most efficient estimates of regression parameters. In this paper, we propose a multi-wave sampling design to approximate the optimal design for design-based estimators. Influences functions are used to compute the optimal sampling allocations. We propose to use informative priors on regression parameters to derive the wave-1 sampling probabilities because any pre-specified sampling probabilities may be far from optimal and decrease efficiency. Generalised raking is used in statistical analysis. We show that a two-wave sampling with reasonable informative priors will end up with higher precision for the parameter of interest and be close to the underlying optimal design.
