论文标题
部分可观测时空混沌系统的无模型预测
Design-based composite estimation of small proportions in small domains
论文作者
论文摘要
传统的直接估计方法对于样本量较小的调查人群的域不有效。为了估计域比例，我们将直接估计器和基于域级辅助信息的回归合成估计器结合在一起。对于微小的真实比例，我们介绍了基于设计的线性组合，该组合是基于Fay-Herriot模型的经验最佳线性无偏预测指标（EBLUP）的可靠替代方法。我们还考虑了一种自适应过程，以优化样品尺寸依赖性复合估计器，该估计量取决于所有域的单个参数。我们模仿了立陶宛劳动力调查，在该调查中，我们估计了在市政当局中失业和就业的比例。我们展示了考虑的基于设计的构图和其均方误差的估计量在eblup及其准确性估计方面具有竞争力。
Traditional direct estimation methods are not efficient for domains of a survey population with small sample sizes. To estimate the domain proportions, we combine the direct estimators and the regression-synthetic estimators based on domain-level auxiliary information. For the case of small true proportions, we introduce the design-based linear combination that is a robust alternative to the empirical best linear unbiased predictor (EBLUP) based on the Fay--Herriot model. We also consider an adaptive procedure optimizing a sample-size-dependent composite estimator, which depends on a single parameter for all domains. We imitate the Lithuanian Labor Force Survey, where we estimate the proportions of the unemployed and employed in municipalities. We show where the considered design-based compositions and estimators of their mean square errors are competitive for EBLUP and its accuracy estimation.
