statsmodels.discrete.discrete_model.Probit.score_obs¶
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Probit.
score_obs
(params)[source]¶ Probit model Jacobian for each observation
Parameters: params : array-like
The parameters of the model
Returns: jac : ndarray, (nobs, k_vars)
The derivative of the loglikelihood for each observation evaluated at params.
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.