Maximum Likelihood Estimation of Poisson Model
This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset and zero-inflation.
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.
There are numerical problems if there is no zero-inflation.
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. |
| reduceparams(params) | |
| score(params) | Gradient of log-likelihood evaluated at params |