Conditional Akaike information criterion in the Fay-Herriot model

被引:13
|
作者
Han, Bing [1 ]
机构
[1] RAND Corp, Santa Monica, CA 90407 USA
关键词
Fay-Herriot model; Akaike information; Conditional AIC; SMALL-AREA ESTIMATION; VARIABLE SELECTION;
D O I
10.1016/j.stamet.2012.09.002
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The Fay-Herriot model, a popular approach in small area estimation, uses relevant covariates to improve the inference for quantities of interest in small sub-populations. The conditional Akaike information (AI) (Vaida and Blanchard, 2005 [23]) in linear mixed-effect models with i.i.d. errors can be extended to the Fay-Herriot model for measuring prediction performance. In this paper, we derive the unbiased conditional AIC (cAIC) for three popular approaches to fitting the Fay-Herriot model. The three cAIC have closed forms and are convenient to implement. We conduct a simulation study to demonstrate their accuracy in estimating the conditional AI and superior performance in model selection than the classic AIC. We also apply the cAIC in estimating county-level prevalence rates of obesity for working-age Hispanic females in California. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:53 / 67
页数:15
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