statsmodels.sandbox.distributions.extras.SkewNorm2_gen

class statsmodels.sandbox.distributions.extras.SkewNorm2_gen(momtype=1, a=None, b=None, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, extradoc=None, seed=None)[source]

univariate Skew-Normal distribution of Azzalini

class follows scipy.stats.distributions pattern

Methods

cdf(x, *args, **kwds) Cumulative distribution function of the given RV.
entropy(*args, **kwds) Differential entropy of the RV.
expect([func, args, loc, scale, lb, ub, …]) Calculate expected value of a function with respect to the distribution by numerical integration.
fit(data, *args, **kwds) Return MLEs for shape (if applicable), location, and scale parameters from data.
fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments.
freeze(*args, **kwds) Freeze the distribution for the given arguments.
interval(alpha, *args, **kwds) Confidence interval with equal areas around the median.
isf(q, *args, **kwds) Inverse survival function (inverse of sf) at q of the given RV.
logcdf(x, *args, **kwds) Log of the cumulative distribution function at x of the given RV.
logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV.
logsf(x, *args, **kwds) Log of the survival function of the given RV.
mean(*args, **kwds) Mean of the distribution.
median(*args, **kwds) Median of the distribution.
moment(n, *args, **kwds) n-th order non-central moment of distribution.
nnlf(theta, x) Return negative loglikelihood function.
pdf(x, *args, **kwds) Probability density function at x of the given RV.
ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV.
rvs(*args, **kwds) Random variates of given type.
sf(x, *args, **kwds) Survival function (1 - cdf) at x of the given RV.
stats(*args, **kwds) Some statistics of the given RV.
std(*args, **kwds) Standard deviation of the distribution.
var(*args, **kwds) Variance of the distribution.

Attributes

random_state Get or set the RandomState object for generating random variates.