In the Calculus.jl package, there is a hessian function where you can stick in you likelihood and the estimate.
2014-02-05 Bradley Fay <bradley....@gmail.com>: > I'm trying to figure out how to compute/recover the standard error of the > estimates for a simple linear regression using MLE and simulated. Here is > the code I'm using to generate data, compute the likelihood function, and > optimize the function. > > using Optim > > using Distributions > > > xmat = [ones(10000,1) rand(Normal(2,1),10000)]; #creating matrix of > Independent varibles > > u = rand(Normal(0,1),10000); #create vector of normally distributed errors > mean 0 std 1 > > ymat = xmat*[3,2] + u; #generating dependent variables. > > > start_val = [1.0,1.0,1.0]; #Starting values for MLE estimation. Need to be in > float format. > > > #Beginning of Likelihood function > > function ll(param); > > b1=param[1]; > > b2=param[2]; > > sig=param[3]; > ee = ymat-xmat*[b1,b2]; > > loglik = -0.5*log(2*pi*sig^2)-0.5*(ee.^2)/sig^2; > > -sum(loglik); > > end; > > > optimize(ll,start_val) > > > I'm more familiar with Gauss and MATLAB as I just started playing with > Julia yesterday. To compute the standard errors in the identical situation > in MATLAB, I can directly compute the square root of the diagonal of the > inverse of the numerical hessian because Matlab will return the hessian. > mlese=sqrt(diag(inv(hessian))); #MATLAB Code for computing standard errors > > Is there a way to do this in Julia? I'm having trouble figuring this out. > Thanks! > > -- Med venlig hilsen Andreas Noack Jensen
