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([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. |
| 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 |