Bicop.select

Bicop.select(self: pyvinecopulib.Bicop, data: numpy.ndarray[numpy.float64[m, n]], controls: pyvinecopulib.FitControlsBicop = FitControlsBicop()) → None

Selects the best fitting model.

The function calls Bicop.fit() for all families in family_set and selecting the best fitting model by either BIC or AIC, see Bicop.bic() and Bicop.aic().

When at least one variable is discrete, two types of “observations” are required: the first \(n \times 2\) block contains realizations of \(F_{X_1}(X_1), F_{X_2}(X_2)\). Let \(k\) denote the number of discrete variables (either one or two). Then the second \(n \times k\) block contains realizations of \(F_{X_k}(X_k^-)\). The minus indicates a left-sided limit of the cdf. For continuous variables the left limit and the cdf itself coincide. For, e.g., an integer-valued variable, it holds \(F_{X_k}(X_k^-) = F_{X_k}(X_k - 1)\).

Incomplete observations (i.e., ones with a NaN value) are discarded.

Parameters:

• data – An \(n \times (2 + k)\) matrix of observations contained in \((0, 1)\), where \(k\) is the number of discrete variables.

• controls – The controls (see FitControlsBicop).