statsmodels.miscmodels.count.PoissonGMLE

class statsmodels.miscmodels.count.PoissonGMLE(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds)

Maximum Likelihood Estimation of Poisson Model

This is an example for generic MLE which has the same statistical model as discretemod.Poisson.

Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation.

Methods

expandparams(params)	expand to full parameter array when some parameters are fixed
fit([start_params, method, maxiter, …])	Fit the model using maximum likelihood.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
hessian(params)	Hessian of log-likelihood evaluated at params
hessian_factor(params[, scale, observed])	Weights for calculating Hessian
information(params)	Fisher information matrix of model.
initialize()	Initialize (possibly re-initialize) a Model instance.
loglike(params)	Log-likelihood of model at params
loglikeobs(params)	Log-likelihood of individual observations at params
nloglike(params)	Negative log-likelihood of model at params
nloglikeobs(params)	Loglikelihood of Poisson model
predict(params[, exog])	After a model has been fit predict returns the fitted values.
predict_distribution(exog)	return frozen scipy.stats distribution with mu at estimated prediction
reduceparams(params)	Reduce parameters
score(params)	Gradient of log-likelihood evaluated at params
score_obs(params, **kwds)	Jacobian/Gradient of log-likelihood evaluated at params for each observation.

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.