Observed best selective prediction in small area estimation
Observed best selective prediction in small area estimation
复制标题
小区域估计中观察到的最佳选择性预测
DOI：
10.1016/j.jmva.2019.04.002
复制
发表时间：
2019
影响因子：
1.6
通讯作者：
G. S.
中科院分区：
文献类型：
--
作者：
Sugasawa;S.;Kawakubo;Y. and Datta;G. S.
在小区域估计方法中，通常分别考虑合适协变量的选择和所选模型中的估计。在本文中，我们考虑变量的选择和估计同时最小化的总均方预测误差（MSPE）的小区域平均值的估计。衍生的方法，我们称之为观察最佳选择性预测（OBSP），可以视为Jiang等人（2011）观察最佳预测（OBP）方法的扩展。当真实模型包含在最大模型中时，得到的OBSP估计量是一致的。在此基础上，我们利用参数自助法得到了MSPE的一个估计。通过仿真实验，我们研究了OBSP与OBP的有限样本性能，其中变量选择是通过使用AIC和BIC进行的，OBP使用所有的协变量。作为一个例子，我们将OBSP应用于日本的调查数据。
In small area estimation methodology, selection of the suitable covariates and estimation in the selected model are usually considered separately. In this paper, we consider variable selection and estimation simultaneously to minimize the total mean squared prediction errors (MSPE) for estimation of small area means. The derived method, which we call observed best selective prediction (OBSP), can be regarded as an extension of the observed best prediction (OBP) method by Jiang et al. (2011). When the true model is included in the largest model, the resulting OBSP estimator is consistent. Based on the asymptotic result, we derive an estimator of MSPE by applying the parametric bootstrap method. Through simulation experiments, we investigate the finite-sample performance of OBSP together with OBP in which the variable selection is carried out by using AIC and BIC, and OBP using all the covariates. As an example, we applied OBSP to Japanese survey data.