New ways to combine data from multiple environmental area frame surveys of a finite population are being introduced. Environmental surveys often sample finite populations through area frames. However, to combine multiple surveys without risking bias, design components (inclusion probabilities, etc.) are needed at unit level of the finite population. We show how to derive the design components and exemplify this for three commonly used area frame sampling designs. We show how to produce an unbiased estimator using data from multiple surveys, and how to reduce the risk of introducing significant bias in linear combinations of estimators from multiple surveys. If separate estimators and variance estimators are used in linear combinations, there’s a risk of introducing negative bias. By using pooled variance estimators, the bias of a linear combination estimator can be reduced. National environmental surveys often provide good estimators at national level, while being too sparse to provide sufficiently good estimators for some domains. With the proposed methods, one can plan extra sampling efforts for such domains, without discarding readily available information from the aggregate/national survey. Through simulation, we show that the proposed methods are either unbiased, or yield low variance with small bias, compared to traditionally used methods.

中文翻译：

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结合有限人口的环境区域框架调查

正在引入将来自有限人口的多个环境区域框架调查的数据结合起来的新方法。环境调查通常通过区域框架对有限的人口进行抽样。然而，为了在不存在偏差风险的情况下组合多项调查，在有限总体的单位级别需要设计组件（包含概率等）。我们展示了如何导出设计组件并举例说明了三个常用的区域框架抽样设计。我们展示了如何使用来自多个调查的数据生成无偏估计，以及如何降低在来自多个调查的估计的线性组合中引入显着偏倚的风险。如果在线性组合中使用单独的估计量和方差估计量，则存在引入负偏差的风险。通过使用合并方差估计量，可以减少线性组合估计器的偏差。国家环境调查通常会在国家层面提供良好的估计量，但过于稀疏而无法为某些领域提供足够好的估计量。使用提议的方法，人们可以为这些领域计划额外的抽样工作，而不会丢弃来自汇总/国家调查的现成信息。通过模拟，我们表明，与传统使用的方法相比，所提出的方法要么是无偏的，要么产生具有小偏差的低方差。不丢弃汇总/全国调查中现成的信息。通过模拟，我们表明，与传统使用的方法相比，所提出的方法要么是无偏的，要么产生具有小偏差的低方差。不丢弃汇总/全国调查中现成的信息。通过模拟，我们表明，与传统使用的方法相比，所提出的方法要么是无偏的，要么产生具有小偏差的低方差。