Displaying 2 results from an estimated 2 matches for "lpl1".
Did you mean:
cpl1
2010 Dec 09
1
survival: ridge log-likelihood workaround
Dear all,
I need to calculate likelihood ratio test for ridge regression. In February I have reported a bug where coxph returns unpenalized log-likelihood for final beta estimates for ridge coxph regression. In high-dimensional settings ridge regression models usually fail for lower values of lambda. As the result of it, in such settings the ridge regressions have higher values of lambda (e.g.
2010 Dec 11
0
is there a packge or code to generate markov chains in R
...nt of coxph is the LPL for
the reported coefficients.
For a ridge regression the coxph function maximizes LPL(beta) -
penalty(beta) = penalized partial likelihood = PPL(beta). You have
correctly recreated the PPL.
Second: how do you do formal tests on such a model? This is hard. The
difference LPL1- LPL2 is a chi-square when each is the result of
maximizing the Cox LPL over a set of coefficients; when using a PPL we
are maximizing over something else. The distribution of the difference
of constrained LPL values can be argued to be a weighed sum of squared
normals where the weights are in (0,...