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
Containing all information, environment of pendensity()
lambda0
penalty parameter lambda
opt.Likelihood
optimal unpenalized likelihood of the density estimation
Value
myAICsum of the negative unpenalized log likelihood and mytrace
mytracecalculated 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
Details
AIC is calculated as
$AIC(\lambda)= - l(\hat{\beta}) + df(\lambda)$
References
Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.