# statsmodels.nonparametric.kde.KDEUnivariate¶

class statsmodels.nonparametric.kde.KDEUnivariate(endog)[source]

Univariate Kernel Density Estimator.

Parameters
endogarray_like

The variable for which the density estimate is desired.

KDEMultivariate
kdensity, kdensityfft

Notes

If cdf, sf, cumhazard, or entropy are computed, they are computed based on the definition of the kernel rather than the FFT approximation, even if the density is fit with FFT = True.

KDEUnivariate is much faster than KDEMultivariate, due to its FFT-based implementation. It should be preferred for univariate, continuous data. KDEMultivariate also supports mixed data.

Examples

>>> import statsmodels.api as sm
>>> import matplotlib.pyplot as plt

>>> nobs = 300
>>> np.random.seed(1234)  # Seed random generator
>>> dens = sm.nonparametric.KDEUnivariate(np.random.normal(size=nobs))
>>> dens.fit()
>>> plt.plot(dens.cdf)
>>> plt.show()


Methods

 evaluate(point) Evaluate density at a single point. fit([kernel, bw, fft, weights, gridsize, …]) Attach the density estimate to the KDEUnivariate class.

Properties

 cdf Returns the cumulative distribution function evaluated at the support. cumhazard Returns the hazard function evaluated at the support. entropy Returns the differential entropy evaluated at the support icdf Inverse Cumulative Distribution (Quantile) Function sf Returns the survival function evaluated at the support.