statsmodels.discrete.discrete_model.Probit.score_obs

Probit.score_obs(params)

Probit model Jacobian for each observation

Parameters:params (array-like) – The parameters of the model Returns:jac – The derivative of the loglikelihood for each observation evaluated at params. Return type:array-like

Notes

$$\frac{\partial\ln L_{i}}{\partial\beta}=\left[\frac{q_{i}\phi\left(q_{i}x_{i}^{\prime}\beta\right)}{\Phi\left(q_{i}x_{i}^{\prime}\beta\right)}\right]x_{i}$$

for observations $i=1,...,n$

Where $q=2y-1$. This simplification comes from the fact that the normal distribution is symmetric.