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statsmodels.miscmodels.count.PoissonZiGMLE

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.

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]) Create a Model from a formula and dataframe.
hessian(params) Hessian of log-likelihood evaluated at params
information(params) Fisher information matrix of model
initialize()
jac(*args, **kwds) jac is deprecated, use score_obs instead!
loglike(params)
loglikeobs(params)
nloglike(params)
nloglikeobs(params) Loglikelihood of Poisson model
predict(params[, exog]) After a model has been fit predict returns the fitted values.
reduceparams(params)
score(params) Gradient of log-likelihood evaluated at params
score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.

Attributes

endog_names
exog_names

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