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.
Methods
expandparams(params) | expand to full parameter array when some parameters are fixed |
fit([start_params, method, maxiter, ...]) | Fit the model using maximum likelihood. |
from_formula(formula, data[, subset]) | Create a Model from a formula and dataframe. |
hessian(params) | Hessian of log-likelihood evaluated at params |
information(params) | Fisher information matrix of model |
initialize() | |
jac(*args, **kwds) | jac is deprecated, use score_obs instead! |
loglike(params) | |
loglikeobs(params) | |
nloglike(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) | |
score(params) | Gradient of log-likelihood evaluated at params |
score_obs(params, **kwds) | Jacobian/Gradient of log-likelihood evaluated at params for each observation. |
Attributes
endog_names | |
exog_names |