# 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: 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.