def fit(self, start_params=None, maxiter=1000, disp=False, method='bfgs', **kwds):
    '\n        Fit the model by maximum likelihood.\n\n        Parameters\n        ----------\n        start_params : array-like\n            A vector of starting values for the regression\n            coefficients.  If None, a default is chosen.\n        maxiter : integer\n            The maximum number of iterations\n        disp : bool\n            Show convergence stats.\n        method : str\n            The optimization method to use.\n        kwds :\n            Keyword arguments for the optimizer.\n\n        Returns\n        -------\n        BetaResults instance.\n        '
    if (start_params is None):
        start_params = self._start_params()
    if ('cov_type' in kwds):
        if (kwds['cov_type'].lower() == 'eim'):
            self.hess_type = 'eim'
            del kwds['cov_type']
    else:
        self.hess_type = 'oim'
    res = super(BetaModel, self).fit(start_params=start_params, maxiter=maxiter, method=method, disp=disp, **kwds)
    if (not isinstance(res, BetaResultsWrapper)):
        res = BetaResultsWrapper(res)
    return res
Fit the model by maximum likelihood.

Parameters
----------
start_params : array-like
    A vector of starting values for the regression
    coefficients.  If None, a default is chosen.
maxiter : integer
    The maximum number of iterations
disp : bool
    Show convergence stats.
method : str
    The optimization method to use.
kwds :
    Keyword arguments for the optimizer.

Returns
-------
BetaResults instance.
def fit(self, start_params=None, maxiter=1000, disp=False, method='bfgs', **kwds):
    '\n        Fit the model by maximum likelihood.\n\n        Parameters\n        ----------\n        start_params : array-like\n            A vector of starting values for the regression\n            coefficients.  If None, a default is chosen.\n        maxiter : integer\n            The maximum number of iterations\n        disp : bool\n            Show convergence stats.\n        method : str\n            The optimization method to use.\n        kwds :\n            Keyword arguments for the optimizer.\n\n        Returns\n        -------\n        BetaResults instance.\n        '
    if (start_params is None):
        start_params = self._start_params()
    if ('cov_type' in kwds):
        if (kwds['cov_type'].lower() == 'eim'):
            self.hess_type = 'eim'
            del kwds['cov_type']
    else:
        self.hess_type = 'oim'
    res = super(BetaModel, self).fit(start_params=start_params, maxiter=maxiter, method=method, disp=disp, **kwds)
    if (not isinstance(res, BetaResultsWrapper)):
        res = BetaResultsWrapper(res)
    return res