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) [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. est_loc_scale(*args, **kwds)

statsmodels.robust.robust_linear_model.RLMResults class statsmodels.robust.robust_linear_model.RLMResults(model, params, normalized_cov_params, scale) [source] Class to contain RLM results Returns: **Attributes** : bcov_scaled : array p x p scaled covariance matrix specified in the model fit method. The default is H1. H1 is defined as k**2 * (1/df_resid*sum(M.psi(sresid)**2)*scale**2)/ ((1/nobs*sum(M.psi_deriv(sresid)))**2) * (X.T X)^(-1) where k = 1 + (df_model +1)/nobs * var_psiprime/m**

statsmodels.genmod.cov_struct.CovStruct.summary CovStruct.summary() [source] Returns a text summary of the current estimate of the dependence structure.

statsmodels.sandbox.regression.gmm.IVRegressionResults.conf_int IVRegressionResults.conf_int(alpha=0.05, cols=None) Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return Notes The confidence interval is based on Student?s t-distribution.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.est_loc_scale LogTransf_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

statsmodels.discrete.discrete_model.BinaryModel.score BinaryModel.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.discrete.discrete_model.DiscreteResults.initialize DiscreteResults.initialize(model, params, **kwd)

statsmodels.sandbox.distributions.transformed.ExpTransf_gen class statsmodels.sandbox.distributions.transformed.ExpTransf_gen(kls, *args, **kwargs) [source] Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_

Vector Autoregressions tsa.vector_ar VAR(p) processes We are interested in modeling a multivariate time series , where denotes the number of observations and the number of variables. One way of estimating relationships between the time series and their lagged values is the vector autoregression process: where is a coefficient matrix. We follow in large part the methods and notation of Lutkepohl (2005), which we will not develop here. Model fitting Note The classes referenced below ar

statsmodels.stats.weightstats.DescrStatsW.sum_weights static DescrStatsW.sum_weights() [source]