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.