I am doing multivariate nonparametric kernel regression using the Python function as mentioned in the title. The documentation can be found here: https://www.statsmodels.org/stable/generated/statsmodels.nonparametric.kernel_regression.KernelReg.html#statsmodels.nonparametric.kernel_regression.KernelReg
As far as my understanding goes, kernel regression requires to specify a kernel, such as Gaussian (sometimes called RBF). However, this function interface does not seem to allow me to specify the kernel other than 'll'(locally linear), 'lc'(locally constant), unless I am missing something. Can anybody explain to me what kind of kernel is used for 'll' or 'lc' specifications? Are there any other kernels I can choose from, where the bandwidth can be automatically chosen?
More generally, what python tool would you recommend for a nonparametric regression with multiple predictors, and relatively large number of samples (10000+)? With a mere 1000 samples and two predictors, the afore-mentioned tool already is taking quite some time. I have dozens of regressions to run, therefore I appreciate good speed and smoothness more than sophistication. My data are simulated and plenty. So my work is almost an noisy interpolation rather than regression. However, linear regression is definitely not going to work.