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(**kwargs[, start_params, method, ...]) | Fit the model using maximum likelihood. |
hessian(params) | Hessian of log-likelihood evaluated at params |
information(params) | Fisher information matrix of model |
initialize() | |
jac(params, **kwds) | Jacobian/Gradient of log-likelihood evaluated at params for each |
loglike(params) | |
loglikeobs(params) | |
nloglike(params) | |
nloglikeobs(params) | Loglikelihood of Poisson model |
predict(exog[, params]) | 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 |