Automated selection of post‐strata using a model‐assisted regression tree estimator
Automated selection of post‐strata using a model‐assisted regression tree estimator
复制标题
使用模型辅助回归树估计器自动选择后层
DOI：
10.1111/sjos.12356
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发表时间：
2017
影响因子：
1
通讯作者：
Daniell Toth
中科院分区：
文献类型：
--
作者：
K. McConville;Daniell Toth
尽管具有理想的性质，但模型辅助估计量很少以任何形式使用，而是以最简单的形式产生官方统计数据。这是因为更复杂的模型往往不适合现有的辅助数据。在模型辅助框架下，我们提出了有限总体的回归树估计。回归树模型擅长处理抽样框架中通常可用的辅助数据类型，并提供易于解释和证明的模型。该估计量可以被视为后分层估计量，其中后分层由回归树的递归划分算法自动选择。我们建立了回归树估计量和方差估计量的相合性，沿着建立了回归树估计量的渐近正态性。我们使用美国劳工统计局职业就业统计调查数据比较我们的估计器与其他调查估计器的性能。
Despite having desirable properties, model‐assisted estimators are rarely used in anything but their simplest form to produce official statistics. This is due to the fact that the more complicated models are often ill suited to the available auxiliary data. Under a model‐assisted framework, we propose a regression tree estimator for a finite‐population total. Regression tree models are adept at handling the type of auxiliary data usually available in the sampling frame and provide a model that is easy to explain and justify. The estimator can be viewed as a post‐stratification estimator where the post‐strata are automatically selected by the recursive partitioning algorithm of the regression tree. We establish consistency of the regression tree estimator and a variance estimator, along with asymptotic normality of the regression tree estimator. We compare the performance of our estimator to other survey estimators using the United States Bureau of Labor Statistics Occupational Employment Statistics Survey data.