IC.SVC_mle function

Conditional Akaike's and Bayesian Information Criteria

Methods to calculate information criteria for SVC_mle objects. Currently, two are supported: the conditional Akaike's Information Criteria $cAIC = -2*log-likelihood + 2*(edof + df)$

and the Bayesian Information Criteria $BIC = -2*log-likelihood + log(n) * npar$. Note that the Akaike's Information Criteria is of the corrected form, that is: $edof$ is the effective degrees of freedom which is derived as the trace of the hat matrices and df is the degree of freedoms with respect to mean parameters.

## S3 method for class 'SVC_mle'
BIC(object, ...)

## S3 method for class 'SVC_mle'
AIC(object, conditional = "BW", ...)

Arguments

• object: SVC_mle object
• ...: further arguments
• conditional: string. If conditional = "BW", the conditional AIC is calculated.

Returns

numeric, value of information criteria

Author(s)

Jakob Dambon