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statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.cdf

KDEMultivariateConditional.cdf(endog_predict=None, exog_predict=None)[source]

Cumulative distribution function for the conditional density.

Parameters:

endog_predict: array_like, optional :

The evaluation dependent variables at which the cdf is estimated. If not specified the training dependent variables are used.

exog_predict: array_like, optional :

The evaluation independent variables at which the cdf is estimated. If not specified the training independent variables are used.

Returns:

cdf_est: array_like :

The estimate of the cdf.

Notes

For more details on the estimation see [R14], and p.181 in [R13].

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

..math:: F(y|x)=frac{n^{-1}sum_{i=1}^{n}G(frac{y-Y_{i}}{h_{0}})
W_{h}(X_{i},x)}{widehat{mu}(x)}

where G() is the product kernel CDF estimator for the dependent (y) variable(s) and W() is the product kernel CDF estimator for the independent variable(s).

References

[R13](1, 2) Racine, J., Li, Q. Nonparametric econometrics: theory and practice. Princeton University Press. (2007)
[R14](1, 2) Liu, R., Yang, L. “Kernel estimation of multivariate cumulative distribution function.” Journal of Nonparametric Statistics (2008)

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