variance.par: Calculating the variance of the parameters
In pendensity: Density Estimation with a Penalized Mixture Approach

Description Usage Arguments Details Value Author(s) References

Calculating the variance of the parameters of the estimation, depending on the second order derivative and the penalized second order derivative of the density estimation.

1	variance.par(penden.env)

penden.env

Containing all information, environment of pendensity()

The variance of the parameters of the estimation results as the product of the inverse of the penalized second order derivative times the second order derivative without penalization time the inverse of the penalized second order derivative.

V(β, λ_0)=I_p^{-1}(β, λ) I_p(β, λ=0) I_p^{-1}(β, λ) with I_p(β^{-1}, λ)=E_{f(y)}\bigl\{J_p(β, λ)\bigr\}

The needed values are saved in the environment.

The return is a variance matrix of the dimension (K-1)x(K-1).

Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>

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.