Generic function calculating the Akaike information criterion for one model objects of ibr class for which a log-likelihood value can be obtained, according to the formula -2 log(sigma^2) + k df/n, where df represents the effective degree of freedom (trace) of the smoother in the fitted model, and k = 2 for the usual AIC, or k = \log(n) (n the number of observations) for the so-called BIC or SBC (Schwarz's Bayesian criterion).
A fitted model object of class ibr.
Numeric, the penalty per parameter to be used; the
The ibr method for
log(sigma^2)+2*df/n, where df is the trace
of the smoother.
returns a numeric value
with the corresponding AIC (or BIC, or ..., depending on
Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.
Hurvich, C. M., Simonoff J. S. and Tsai, C. L. (1998) Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion. Journal of the Royal Statistical Society, Series B, 60, 271-293 .
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