Login Register

Poisson.hessian()

statsmodels.discrete.discrete_model.Poisson.hessian

Poisson.hessian(params) [source]

Poisson model Hessian matrix of the loglikelihood

Parameters:

params : array-like

The parameters of the model

Returns:

hess : ndarray, (k_vars, k_vars)

The Hessian, second derivative of loglikelihood function, evaluated at params

Notes

$\frac{\partial^{2}\ln L}{\partial\beta\partial\beta^{\prime}}=-\sum_{i=1}^{n}\lambda_{i}x_{i}x_{i}^{\prime}$

where the loglinear model is assumed

$\ln\lambda_{i}=x_{i}\beta$

Links:

http://statsmodels.sourceforge.net/stable/generated/statsmodels.discrete.discrete_model.Poisson.hessian.html

doc_statsmodels

2017-01-18 16:13:51

Comments

Leave a Comment

Please login to continue.

Popular Articles

IV2SLS.hessian() statsmodels.sandbox.regression.gmm.IV2SLS.hessian IV2SLS.hessian(params) The Hessian matrix of the model

CountModel.hessian() statsmodels.discrete.discrete_model.CountModel.hessian CountModel.hessian(params) The Hessian matrix of the model

PoissonOffsetGMLE.hessian() statsmodels.miscmodels.count.PoissonOffsetGMLE.hessian PoissonOffsetGMLE.hessian(params) Hessian of log-likelihood evaluated at params

MixedLM.hessian() statsmodels.regression.mixed_linear_model.MixedLM.hessian MixedLM.hessian(params) [source] Returns the Hessian matrix of the profile log-likelihoo

PoissonZiGMLE.hessian() statsmodels.miscmodels.count.PoissonZiGMLE.hessian PoissonZiGMLE.hessian(params) Hessian of log-likelihood evaluated at params