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Information criteria for Fay–Herriot model selection

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Marhuenda García, Yolanda and Morales, Domingo and Pardo Llorente, María del Carmen (2014) Information criteria for Fay–Herriot model selection. Computational Statistics and Data Analysis, 70 . pp. 268-280. ISSN 0167-9473

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Official URL: http://www.sciencedirect.com/science/article/pii/S016794731300340X?np=y


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Abstract

The selection of an appropriate model is a fundamental step of the data analysis in small area estimation. Bias corrections to the Akaike information criterion, AIC, and to the Kullback symmetric divergence criterion, KIC, are derived for the Fay–Herriot model. Furthermore, three bootstrap-corrected variants of AIC and of KIC are proposed. The performance of the eight considered criteria is investigated with a simulation study and an application to real data. The obtained results suggest that there are better alternatives than the classical AIC.


Item Type:Article
Uncontrolled Keywords:Small area estimation; Fay–Herriot model; Akaike information criterion; Kullback symmetric divergence criterion; Model selection; Bootstrap
Subjects:Sciences > Mathematics > Mathematical statistics
ID Code:37280
Deposited On:28 Apr 2016 10:26
Last Modified:03 May 2016 10:33

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