statsmodels.nonparametric.kde.KDEUnivariate.fit¶

KDEUnivariate.fit(kernel='gau', bw='normal_reference', fft=True, weights=None, gridsize=None, adjust=1, cut=3, clip=(-inf, inf))[source]¶

Attach the density estimate to the KDEUnivariate class.

Parameters:¶

kernelstr

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

bwstr, float, callable

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

fftbool

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.

gridsizeint

If gridsize is None, max(len(x), 50) is used.

cutfloat

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 * bw and max(x) + cut * adjust * bw.

adjustfloat

An adjustment factor for the bw. Bandwidth becomes bw * adjust.

Returns:¶

KDEUnivariate

The instance fit,