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)[source]¶

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.

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
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 the model for all 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.