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I am estimating a survival model with MLE. I use optim to maximize the likelihood function, and I intend to use the Hessian matrix returned by optim to get the standard errors (which lie on the diagonal of the inverse of Hessian). As it turns out that the Hessian matrix is singular and can not be inverted by R's default inverse function base::solve(). I can invert my Hessian using generalized inverse function MASS::ginv() though. What concerns me is that I got many very small standard errors, which render my coefficients suspiciously significant.

What do you think of using ginv() instead of solve() to invert Hessian for inference?

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  • $\begingroup$ Which model are you fitting using optim? Does optim converge? $\endgroup$ – Simon Boge Brant Oct 18 '18 at 13:21
  • $\begingroup$ It's a split survival model. Optim did converge. $\endgroup$ – bellmaneqn Oct 18 '18 at 13:23
  • $\begingroup$ Did you try to use likelihood profiling? to get confidence intervals for the specific parameter(s) that you are interested in? $\endgroup$ – kjetil b halvorsen May 31 at 15:08

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