Estimate the sampling distribution of the MLE for a likelihood model. — sampler.likelihood

We use the bootstrap method. In other words, we treat the data as an empirical distribution and sample from it to get a new dataset, then we fit the model to that dataset and return the MLE. We do this R times and return the R MLEs.

# S3 method for likelihood_model
sampler(model, df, par, ..., nthreads = 1L)

Arguments

model: The likelihood model
...: Additional arguments to pass into the likelihood model
nthreads: The number of threads to use for parallelization

Value

A function that returns an bootstrapped sampling distribution of an MLE.

Details

This is the default method, but if you want to use a different method, you should define your own method for your likelihood model.