statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.cdf
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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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