Univariate Kernel Density Estimator.
endog : array-like
KDEMultivariate, kdensity, kdensityfft
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.
>>> 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()
|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|
|evaluate(point)||Evaluate density at a single point.|
|fit([kernel, bw, fft, weights, gridsize, ...])||Attach the density estimate to the KDEUnivariate class.|
|icdf()||Inverse Cumulative Distribution (Quantile) Function|
|sf()||Returns the survival function evaluated at the support.|