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 $\mu$ $\mu$ , for which competing models lead to different estimates, say ${\hat {\mu }}_{j}$ ${\hat {\mu }}_{j}$ for model $j$ $j$ . The FIC method consists in first developing an exact or approximate expression for the precision or quality of each estimator, say $r_{j}$ $r_{j}$ for ${\hat {\mu }}_{j}$ ${\hat {\mu }}_{j}$ , and then use data to estimate these precision measures, say ${\hat {r}}_{j}$ ${\hat {r}}_{j}$ . In the end the model with best estimated precision is selected. The FIC methodology was developed by Gerda Claeskens and Nils Lid Hjort, first in two 2003 discussion articles in Journal of the American Statistical Association and later on in other papers and in their 2008 book.

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