Attach the density estimate to the KDEUnivariate class.
The Kernel to be used. Choices are:
“biw” for biweight
“cos” for cosine
“epa” for Epanechnikov
“gau” for Gaussian.
“tri” for triangular
“triw” for triweight
“uni” for uniform
The bandwidth to use. Choices are:
“scott” - 1.059 * A * nobs ** (-1/5.), where A is min(std(x),IQR/1.34)
“silverman” - .9 * A * nobs ** (-1/5.), where A is min(std(x),IQR/1.34)
“normal_reference” - C * A * nobs ** (-1/5.), where C is calculated from the kernel. Equivalent (up to 2 dp) to the “scott” bandwidth for gaussian kernels. See bandwidths.py
If a float is given, its value is used as the bandwidth.
If a callable is given, it’s return value is used. The callable should take exactly two parameters, i.e., fn(x, kern), and return a float, where:
x - the clipped input data
kern - the kernel instance used
Whether or not to use FFT. FFT implementation is more computationally efficient. However, only the Gaussian kernel is implemented. If FFT is False, then a ‘nobs’ x ‘gridsize’ intermediate array is created.
If gridsize is None, max(len(x), 50) is used.
Defines the length of the grid past the lowest and highest values of x so that the kernel goes to zero. The end points are
min(x) - cut * adjust * bwand
max(x) + cut * adjust * bw.
An adjustment factor for the bw. Bandwidth becomes bw * adjust.
The instance fit,