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
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
bsejac() | |
bsejhj() | |
covjac() | covariance of parameters based on loglike outer product of jacobian |
covjhj() | |
expandparams(params) | expand to full parameter array when some parameters are fixed |
fit([start_params, method, maxiter, ...]) | Fit the model using maximum likelihood. |
hessian(params) | Hessian of log-likelihood evaluated at params |
hessv() | |
information(params) | Fisher information matrix of model |
initialize() | |
jac(params, **kwds) | Jacobian/Gradient of log-likelihood evaluated at params for each observation. |
jacv() | |
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 |
Attributes
endog_names | |
exog_names |