/Statsmodels

# statsmodels.discrete.discrete_model.MNLogit.hessian

MNLogit.hessian(params) [source]

Multinomial logit Hessian matrix of the log-likelihood

Parameters: params (array-like) – The parameters of the model hess – The Hessian, second derivative of loglikelihood function with respect to the flattened parameters, evaluated at params ndarray, (J*K, J*K)

#### Notes

$\frac{\partial^{2}\ln L}{\partial\beta_{j}\partial\beta_{l}}=-\sum_{i=1}^{n}\frac{\exp\left(\beta_{j}^{\prime}x_{i}\right)}{\sum_{k=0}^{J}\exp\left(\beta_{k}^{\prime}x_{i}\right)}\left[\boldsymbol{1}\left(j=l\right)-\frac{\exp\left(\beta_{l}^{\prime}x_{i}\right)}{\sum_{k=0}^{J}\exp\left(\beta_{k}^{\prime}x_{i}\right)}\right]x_{i}x_{l}^{\prime}$

where $$\boldsymbol{1}\left(j=l\right)$$ equals 1 if j = l and 0 otherwise.

The actual Hessian matrix has J**2 * K x K elements. Our Hessian is reshaped to be square (J*K, J*K) so that the solvers can use it.

This implementation does not take advantage of the symmetry of the Hessian and could probably be refactored for speed.

© 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.MNLogit.hessian.html