statsmodels.regression.process_regression.ProcessMLEResults.bootstrap

ProcessMLEResults.bootstrap(nrep=100, method='nm', disp=0, store=1, rng=None)

Simple bootstrap to get mean and variance of estimator

See notes

Parameters:

nrepint

Number of bootstrap replications.

methodstr

Optimization method to use.

dispbool

If true, then optimization prints results.

storebool

If true, then parameter estimates for all bootstrap iterations are attached in self.bootstrap_results.

rng{None, int, array_like[int], numpy.random.Generator, numpy.random.RandomState}, optional

If rng is None, a new Generator is created using fresh entropy from the operating system. If rng is an int or array of ints, a new Generator is created, seeded with rng. If rng is already a Generator or RandomState instance, that instance is used.

Returns:

meanndarray

Mean of parameter estimates over bootstrap replications.

stdndarray

Standard deviation of parameter estimates over bootstrap replications.

resultsndarray

Parameter estimates for each bootstrap replication.

Notes

This was mainly written to compare estimators of the standard errors of the parameter estimates. It uses independent random sampling from the original endog and exog, and therefore is only correct if observations are independently distributed.

This will be moved to apply only to models with independently distributed observations.