class statsmodels.miscmodels.count.PoissonZiGMLE(endog, exog=None, offset=None, missing='none', **kwds)[source]

Maximum Likelihood Estimation of Poisson Model

This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset and zero-inflation.

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.

There are numerical problems if there is no zero-inflation.


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.
nloglikeobs(params) Loglikelihood of Poisson model
predict(params[, exog]) After a model has been fit predict returns the fitted values.
score(params) Gradient of log-likelihood evaluated at params
score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.


endog_names Names of endogenous variables
exog_names Names of exogenous variables