论文标题
复杂调查中单向方差分析模型的伪贝叶斯估计
Pseudo Bayesian Estimation of One-way ANOVA Model in Complex Surveys
论文作者
论文摘要
我们设计了对主要簇和次要嵌套单元的两阶段信息样本的调查伪后分布估计量，以进行单向方差分析（ANOVA）人群作为一个简单的典范案例，其中人群模型随机效应被定义为基于学生绩效的基于学生的consection and Specuts of Schoolsion和Persons composity complity complity complity consport consport consment of Schoolsive and pross pros pros of Sepparie oec of Squople（诸如2000年OEC（OEC）OEC（oecs oe eec）。我们考虑对观察到的信息样本进行估计，这两者都在增强的伪可能性下，共同样本随机效应以及综合可能性，从调查加权增强的伪可能性中的随机效应边缘化。本文包括一个理论上的博览会，该论述列举了易于验证的条件，该条件可以保证增强伪后验下的估计在真实生成参数上是一致的。我们在模拟中揭示了两种方法都会在群集加权残差内部的关键条件上逐渐产生对随机效应的生成超参数的无偏估计。我们提供了与两个常见替代方案的比较，一种期望最大化方法和需要成对采样权重的复合可能性方法。
We devise survey-weighted pseudo posterior distribution estimators under two-stage informative sampling of both primary clusters and secondary nested units for a one-way analysis of variance (ANOVA) population generating model as a simple canonical case where population model random effects are defined to be coincident with the primary clusters, for example student performance based on a survey of schools and students such as the 2000 OECD Programme for International Student Assessment (PISA). We consider estimation on an observed informative sample under both an augmented pseudo likelihood that co-samples the random effects, as well as an integrated likelihood that marginalizes out the random effects from the survey-weighted augmented pseudo likelihood. This paper includes a theoretical exposition that enumerates easily verified conditions for which estimation under the augmented pseudo posterior is guaranteed to be consistent at the true generating parameters. We reveal in simulation that both approaches produce asymptotically unbiased estimation of the generating hyperparameters for the random effects when a key condition on the sum of within cluster weighted residuals is met. We present a comparison with two frequentist alternatives, an expectation-maximization approach and a composite likelihood method that requires pairwise sampling weights.
