skfolio.distribution.compute_pseudo_observations

skfolio.distribution.compute_pseudo_observations(X)

Compute pseudo-observations by ranking each column of the data and scaling the ranks.

The goal of computing pseudo-observations is to transform your raw data into a form that has uniform marginal distributions on the open interval (0, 1). This is particularly useful in copula modeling and other statistical methods where the dependence structure is of primary interest, independent of the marginal distributions.

This function transforms each column of the input data into pseudo-observations on the (0, 1) interval. For each column, the ranks (starting at 1) are divided by (n_samples + 1) to avoid 0 and 1 values, which are problematic for many copula methods.

Parameters:

Xarray-like of shape (n_observations, n_assets)

Input data.

Returns:

pseudo_observations: ndarray of shape (n_observations, n_assets)

An array of pseudo-observations corresponding to the ranks scaled to (0, 1).