new.beta.val: Calculating the new parameter beta

Description Usage Arguments Details Value Author(s) References

Description

Calculating the direction of the Newton-Raphson step for the known beta and iterate a step size bisection to control the maximizing of the penalized likelihood.

Usage

1
new.beta.val(llold, penden.env)

Arguments

llold

log likelihood of the algorithm one step before

penden.env

Containing all information, environment of pendensity()

Details

We terminate the search for the new beta, when the new log likelihood is smaller than the old likelihood and the step size is smaller or equal 1e-3. We calculate the direction of the Newton Raphson step for the known beta_t and iterate a step size bisection to control the maximizing of the penalized likelihood

\eqn{l(beta,lambda0)}

. This means we set

\eqn{beta[t+1]=beta[t]-(2/v)*sp(beta,lambda0)*(-Jp(beta[t],lambda0))^-1}

with s_p as penalized first order derivative and J_p as penalized second order derivative. We begin with v=0. Not yielding a new maximum for a current v, we increase v step by step respectively bisect the step size. We terminate the iteration, if the step size is smaller than some reference value epsilon (eps=1e-3) without yielding a new maximum. We iterate for new parameter beta until the new log likelihood depending on the new estimated parameter beta differ less than 0.1 log-likelihood points from the log likelihood estimated before.

Value

Likelie

corresponding log likelihood

step

used step size

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.



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