my.AIC {pendensity} | R Documentation |
Calculating the AIC vaule of the density estimation. Therefore, we add the unpenalized log likelihood of the estimation and the degree of freedom, which are
my.AIC(penden.env, lambda0, opt.Likelihood = NULL)
penden.env |
Containing all information, environment of pendensity() |
lambda0 |
penalty parameter lambda |
opt.Likelihood |
optimal unpenalized likelihood of the density estimation |
AIC is calculated as AIC(λ)= - l(hat{β}) + df(λ)
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 invers of the penalized second order derivative of the log likelihood with the unpenalized second order derivative of the log likelihood |
Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>
Penalized Density Estimation, Kauermann G. and Schellhase C. (2009), to appear.