'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.

  • 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
    • 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.