I am doing optimization using maximum likelihood estimation, and when I am trying to get the standard errors of estimates using hessian matrix, I get non-invertible/singular hessian warning.
After I inspect the hessian, I find that there is a column/row of all zeros. What does this tell me? Should I worry about identifiability of my model?
I also have read in the posts that singularity might be caused by precision error, and add $\lambda I$ will fix the problem. But how can I figure it out if my problem is caused by precision in python (I am using float64, and autodifferentiation from jax for hessian)?