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statsmodels.discrete.discrete_model.Logit.hessian

Logit.hessian(params) [source]

Logit model Hessian matrix of the log-likelihood

Parameters: params (array-like) – The parameters of the model
Returns: hess – The Hessian, second derivative of loglikelihood function, evaluated at params
Return type: ndarray, (k_vars, k_vars)

Notes

\[\frac{\partial^{2}\ln L}{\partial\beta\partial\beta^{\prime}}=-\sum_{i}\Lambda_{i}\left(1-\Lambda_{i}\right)x_{i}x_{i}^{\prime}\]

© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.discrete.discrete_model.Logit.hessian.html