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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 : 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}\Lambda_{i}\left(1-\Lambda_{i}\right)x_{i}x_{i}^{\prime}

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