statsmodels.regression.mixed_linear_model.MixedLMResults.bootstrap¶
method
-
MixedLMResults.
bootstrap
(nrep=100, method='nm', disp=0, store=1)¶ 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
- Returns
- meanarray
mean of parameter estimates over bootstrap replications
- stdarray
standard deviation of parameter estimates over bootstrap replications
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.