What is PoissonZiGMLE in the StatsModels library?

This recipe explains what is PoissonZiGMLE in the StatsModels library

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Recipe Objective - What is PoissonZiGMLE in the StatsModels library?

PoissonZiGMLE is a Maximum Likelihood Estimation of Poisson Model. This is an example of generic MLE, which has the same statistical model as discretemod.Poisson but adds offset and zero inflation. All methods and results are standard, except for the definition of the negative log-likelihood method. Gradients and Hessian matrices, and all the resulting statistics, are based on numerical differentiation. If there is no freedom of inflation, there is a numerical problem.

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It belongs to the class statsmodels.miscmodels.count.PoissonZiGMLE(endog, exog=None, offset=None, missing='none', **kwds)

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Table of Contents

Attributes:

endog_names
Names of endogenous variables.

exog_names
Names of exogenous variables.

Methods:

expandparams(params)
expand to full parameter array when some parameters are fixed

fit([start_params, method, maxiter, ...])
Fit method for likelihood based models

from_formula(formula, data[, subset, drop_cols])
Create a Model from a formula and dataframe.

hessian(params)
Hessian of log-likelihood evaluated at params

information(params)
Fisher information matrix of 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

score(params)
Gradient of log-likelihood evaluated at params

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