# 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

 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.