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scikits.statsmodels.discrete.discrete_model.Probit.score

Probit.score(params)[source]

Probit model score (gradient) vector

Parameters :

params : array-like

The parameters of the model

Returns :

The score vector of the model evaluated at `params` :

Notes

\frac{\partial\ln L}{\partial\beta}=\sum_{i=1}^{n}\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}

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

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