Focused information criterion

In statistics, the focused information criterion (FIC) is a method for selecting the most appropriate model among a set of competitors for a given data set. Unlike most other model selection strategies, like the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the deviance information criterion (DIC), the FIC does not attempt to assess the overall fit of candidate models but focuses attention directly on the parameter of primary interest with the statistical analysis, say μ {\displaystyle \mu } , for which competing models lead to different estimates, say μ ^ j {\displaystyle {\hat {\mu }}_{j}} for model j {\displaystyle j} .

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