Conditional Akaike information under covariate shift with application to small area estimation
Conditional Akaike information under covariate shift with application to small area estimation
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DOI：
10.1002/cjs.11354
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发表时间：
2015-01
期刊：
影响因子：
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通讯作者：
Y. Kawakubo;S. Sugasawa;T. Kubokawa
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文献类型：
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作者：
Y. Kawakubo;S. Sugasawa;T. Kubokawa
本文研究了线性混合模型在协变量漂移下固定效应的解释变量的选择问题，即当预测模型中的协变量值与观测数据模型中的协变量值不同时的解释变量的选择问题。我们基于Vaida & Blanchard（2005）提出的条件赤池信息构造了一个变量选择准则。我们特别关注小区域估计中的协变量偏移，并证明了所提出的标准的实用性。此外，还通过模拟研究了数值性能，其中之一是使用真实的地价数据集进行基于设计的模拟。加拿大统计杂志46：316-335; 2018 © 2018加拿大统计学会
In this study, we consider the problem of selecting explanatory variables of fixed effects in linear mixed models under covariate shift, which is when the values of covariates in the model for prediction differ from those in the model for observed data. We construct a variable selection criterion based on the conditional Akaike information introduced by Vaida & Blanchard (2005). We focus especially on covariate shift in small area estimation and demonstrate the usefulness of the proposed criterion. In addition, numerical performance is investigated through simulations, one of which is a design‐based simulation using a real dataset of land prices. The Canadian Journal of Statistics 46: 316–335; 2018 © 2018 Statistical Society of Canada