statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.cdf¶
- KDEMultivariateConditional.cdf(endog_predict=None, exog_predict=None)[source]¶
Cumulative distribution function for the conditional density.
- Parameters:
- endog_predictarray_like,
optional The evaluation dependent variables at which the cdf is estimated. If not specified the training dependent variables are used.
- exog_predictarray_like,
optional The evaluation independent variables at which the cdf is estimated. If not specified the training independent variables are used.
- endog_predictarray_like,
- Returns:
- cdf_estarray_like
The estimate of the cdf.
Notes
For more details on the estimation see [2], and p.181 in [1].
The multivariate conditional CDF for mixed data (continuous and ordered/unordered discrete) is estimated by:
\[F(y|x)=\frac{n^{-1}\sum_{i=1}^{n}G(\frac{y-Y_{i}}{h_{0}}) W_{h}(X_{i},x)}{\widehat{\mu}(x)}\]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