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))

Attach the density estimate to the KDEUnivariate class.

Parameters:

kernel : str

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

bw : str, float

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, it is the bandwidth.

fft : bool

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.

gridsize : int

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

cut : float

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 -/+ cut*bw*{min(X) or max(X)}

adjust : float

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