my.AIC: Calculating the AIC value

In pendensity: Density Estimation with a Penalized Mixture Approach

Description

Calculating the AIC value of the density estimation. Therefore, we add the unpenalized log likelihood of the estimation and the degree of freedom, which are

Usage

my.AIC(penden.env, lambda0, opt.Likelihood = NULL)

Arguments

penden.env	Containing all information, environment of pendensity()
lambda0	penalty parameter lambda
opt.Likelihood	optimal unpenalized likelihood of the density estimation

Details

AIC is calculated as AIC(λ)= - l(\hat{β}) + df(λ)

Value

myAIC	sum of the negative unpenalized log likelihood and mytrace
mytrace	calculated mytrace as the sum of the diagonal matrix df, which results as the product of the inverse of the penalized second order derivative of the log likelihood with the unpenalized second order derivative of the log likelihood

Author(s)

Christian Schellhase <[e-mail]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

pendensity documentation built on May 2, 2019, 3:58 a.m.