AIC.gamBiCop

AIC,gamBiCop-method

An object of the class
<code><a rd-options="gamCopula:gamBiCop-class" href="/link/gamBiCop?package=gamCopula&version=0.0-7&to=gamCopula%3AgamBiCop-class" data-mini-rdoc="gamCopula:gamBiCop-class::gamBiCop">gamBiCop</a></code>.

object

numeric, the penalty per parameter to be used; the default
<code>k = 2</code> is the classical AIC.

Function calculating Akaike's 'An Information Criterion' (AIC) for an object
of the class <code><a rd-options="gamCopula:gamBiCop-class" href="/link/gamBiCop?package=gamCopula&version=0.0-7&to=gamCopula%3AgamBiCop-class" data-mini-rdoc="gamCopula:gamBiCop-class::gamBiCop">gamBiCop</a></code>
(note that the models are usually fitted by penalized likelihood
maximization).

Implementation of various inference and simulation tools to
apply generalized additive models to bivariate dependence structures and
non-simplified vine copulas.

Thibault Vatter

gamCopula

Generalized Additive Models for Bivariate Conditional Dependence
Structures and Vine Copulas

Thomas Nagler

AIC.gamBiCop function

An object of the class
<code><a rd-options='gamCopula:gamBiCop-class' href='gamBiCop'>gamBiCop</a></code>.

Function calculating Akaike's 'An Information Criterion' (AIC) for an object
of the class <code><a rd-options='gamCopula:gamBiCop-class' href='gamBiCop'>gamBiCop</a></code>
(note that the models are usually fitted by penalized likelihood
maximization).

AIC.gamBiCop: Akaike's An Information Criterion for a gamBiCop Object

Description

Usage

Arguments

Value

See Also