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. 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.