AIC extract the Log-Likelihood
and the Akaike's Information Criterion from fitted generalized
hyperbolic distribution objects. The Akaike information criterion is
calculated according to the formula -2 * log-likelihood + k *
npar, where npar represents the number of parameters
in the fitted model, and k = 2 for the usual AIC.
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An object of class
The “penalty” per parameter to be used; the default k = 2 is the classical AIC.
An arbitrary number of objects of class
Either the Log-Likelihood or the Akaike's Information Criterion.
The Log-Likelihood as well as the Akaike's Information Criterion can be obtained from
ghyp.fit.info. However, the benefit of
is that these functions allow a call with an arbitrary number of objects and are better known
because they are generic.
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