Vinecop.scores_cov

Vinecop.scores_cov(self, u: numpy.ndarray, step_wise: bool = True, num_threads: int = 1) → numpy.ndarray

Computes the covariance matrix of scores.

Returns the (mean-centered, divided by n) covariance of the per-observation scores as an \(\mathrm{npars} \times \mathrm{npars}\)matrix. Together with hessian() this forms the sandwich estimator of the asymptotic covariance.

Parameters:

undarray, shape (n, m), dtype float

An \(n \times (d + k)\)or \(n \times 2d\)matrix of evaluation points, where \(k\)is the number of discrete variables (see select()).

step_wisebool

if False, full gradient of the log-likelihood; if True, score function of the step-wise MLE (gradients computed per pair-copula).

num_threadsint

The number of threads to use for computations; if greater than 1, the function will be applied concurrently to num_threads batches of u.