| my.AIC {pendensity} | R Documentation |
Calculating the AIC value
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(\lambda)= - l(\hat{\beta}) + df(\lambda)
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.