3.8.6.2.1. statsmodels.nonparametric.kernel_regression.gpke¶
-
statsmodels.nonparametric.kernel_regression.gpke(bw, data, data_predict, var_type, ckertype='gaussian', okertype='wangryzin', ukertype='aitchisonaitken', tosum=True)[source]¶ - Returns the non-normalized Generalized Product Kernel Estimator
Parameters: bw: 1-D ndarray
The user-specified bandwidth parameters.
- data: 1D or 2-D ndarray
The training data.
- data_predict: 1-D ndarray
The evaluation points at which the kernel estimation is performed.
- var_type: str, optional
The variable type (continuous, ordered, unordered).
- ckertype: str, optional
The kernel used for the continuous variables.
- okertype: str, optional
The kernel used for the ordered discrete variables.
- ukertype: str, optional
The kernel used for the unordered discrete variables.
- tosum : bool, optional
Whether or not to sum the calculated array of densities. Default is True.
Returns: dens: array-like
The generalized product kernel density estimator.
- rac{1}{nh_{1}...h_{q}}sum_{i=1}^
- {n}Kleft(
rac{X_{i}-x}{h} ight)
where
\[K\left(\]rac{X_{i}-x}{h} ight) =
kleft(rac{X_{i1}-x_{1}}{h_{1}} ight) imes
kleft(rac{X_{i2}-x_{2}}{h_{2}} ight) imes... imes
kleft(rac{X_{iq}-x_{q}}{h_{q}} ight)