What is PoissonGMLE in the StatsModels library?

This recipe explains what is PoissonGMLE in the StatsModels library

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Recipe Objective - What is PoissonGMLE in the StatsModels library?

PoissonGMLE is a Maximum Likelihood Estimation of Poisson Model. This is an example of a common MLE with the same statistical model as discretemod.Poisson. All methods and results are common, except for the definition of the negative log-likelihood method. Gradients and Hessian matrices, and all the resulting statistics, are based on numerical differentiation.
It belongs to the class statsmodels.miscmodels.count.PoissonGMLE(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=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

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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