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)

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 the model via 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 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

Methods

expandparams(params)	Expand to full parameter array when some parameters are fixed
fit([start_params, method, maxiter, ...])	Fit the model via 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 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