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.