statsmodels.discrete.discrete_model.Logit.hessian¶

Logit.hessian(params)[source]¶

Logit model Hessian matrix of the log-likelihood

Parameters:¶

paramsarray_like

The parameters of the model

Returns:¶

hessndarray, (k_vars, k_vars)

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

Notes

$$\frac{{\partial }^2\ln L}{\partial \beta \partial {\beta }^{\prime }}=-\sum _i{\mathrm{\Lambda }}_i(1-{\mathrm{\Lambda }}_i)x_i{x}_i^{\prime }$$