statsmodels.nonparametric.kernel_density.KDEMultivariate.cdf

KDEMultivariate.cdf(data_predict=None)[source]

Evaluate the cumulative distribution function.

Parameters

data_predict (array_like, optional) – Points to evaluate at. If unspecified, the training data is used.

Returns

cdf_est – The estimate of the cdf.

Return type

array_like

Notes

See http://en.wikipedia.org/wiki/Cumulative_distribution_function For more details on the estimation see Ref. [5] in module docstring.

The multivariate CDF for mixed data (continuous and ordered/unordered discrete) is estimated by:

\[F(x^{c},x^{d})=n^{-1}\sum_{i=1}^{n}\left[G(\frac{x^{c}-X_{i}}{h})\sum_{u\leq x^{d}}L(X_{i}^{d},x_{i}^{d}, \lambda)\right]\]

where G() is the product kernel CDF estimator for the continuous and L() for the discrete variables.

Used bandwidth is self.bw.