statsmodels.sandbox.distributions.extras.ACSkewT_gen.entropy ACSkewT_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional Location parameter (default=0). scale : array_like, optional Scale parameter (default=1).

statsmodels.sandbox.sysreg.SUR.whiten SUR.whiten(X) [source] SUR whiten method. Parameters: X : list of arrays Data to be whitened. Returns: If X is the exogenous RHS of the system. : ``np.dot(np.kron(cholsigmainv,np.eye(M)),np.diag(X))`` : If X is the endogenous LHS of the system. :

statsmodels.miscmodels.count.PoissonGMLE.loglike PoissonGMLE.loglike(params)

statsmodels.tsa.arima_model.ARMA class statsmodels.tsa.arima_model.ARMA(endog, order, exog=None, dates=None, freq=None, missing='none') [source] Autoregressive Moving Average ARMA(p,q) Model Parameters: endog : array-like The endogenous variable. order : iterable The (p,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should not include a constant or trend. You can spe

statsmodels.sandbox.distributions.extras.pdf_moments_st statsmodels.sandbox.distributions.extras.pdf_moments_st(cnt) [source] Return the Gaussian expanded pdf function given the list of central moments (first one is mean). version of scipy.stats, any changes ? the scipy.stats version has a bug and returns normal distribution

statsmodels.discrete.discrete_model.LogitResults.prsquared static LogitResults.prsquared()

statsmodels.sandbox.distributions.extras.pdf_mvsk statsmodels.sandbox.distributions.extras.pdf_mvsk(mvsk) [source] Return the Gaussian expanded pdf function given the list of 1st, 2nd moment and skew and Fisher (excess) kurtosis. Parameters: mvsk : list of mu, mc2, skew, kurt distribution is matched to these four moments Returns: pdffunc : function function that evaluates the pdf(x), where x is the non-standardized random variable. Notes Changed so it works only if four arguments are

statsmodels.sandbox.distributions.transformed.SquareFunc class statsmodels.sandbox.distributions.transformed.SquareFunc [source] class to hold quadratic function with inverse function and derivative using instance methods instead of class methods, if we want extension to parameterized function Methods derivminus(x) derivplus(x) inverseminus(x) inverseplus(x) squarefunc(x)

statsmodels.sandbox.distributions.transformed.absnormalg statsmodels.sandbox.distributions.transformed.absnormalg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, derivplus,

statsmodels.stats.weightstats.DescrStatsW.demeaned static DescrStatsW.demeaned() [source] data with weighted mean subtracted