my.AIC: Calculating the AIC value

Description Usage Arguments Details Value Author(s) References

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

1
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 <cschellhase@wiwi.uni-bielefeld.de>

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.