statsmodels.nonparametric.kde.KDEUnivariate.fit KDEUnivariate.fit(kernel='gau', bw='normal_reference', fft=True, weights=None, gridsize=None, adjust=1, cut=3, clip=(-inf, inf)) [source] Attach the density estimate to the KDEUnivariate class. Parameters: kernel : str The Kernel to be used. Choices are: ?biw? for biweight ?cos? for cosine ?epa? for Epanechnikov ?gau? for Gaussian. ?tri? for triangular ?triw? for triweight ?uni? for uniform bw : str, float The bandwidth to use. Choices are:

statsmodels.sandbox.distributions.transformed.Transf_gen.var Transf_gen.var(*args, **kwds) Variance of the distribution 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) Returns: var : float the variance of the distribution

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.entropy ExpTransf_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.regression.gmm.IVRegressionResults.pvalues static IVRegressionResults.pvalues()

statsmodels.iolib.summary.Summary.as_csv Summary.as_csv() [source] return tables as string Returns: csv : string concatenated summary tables in comma delimited format

statsmodels.sandbox.distributions.transformed.Transf_gen.mean Transf_gen.mean(*args, **kwds) Mean of the distribution 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) Returns: mean : float the mean of the distribution

statsmodels.sandbox.distributions.transformed.Transf_gen.rvs Transf_gen.rvs(*args, **kwds) Random variates of given type. 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). size : int or tuple of ints, optional Defining number of random variates (default=1). Returns:

statsmodels.genmod.families.family.Family.deviance Family.deviance(endog, mu, scale=1.0) [source] Deviance of (endog,mu) pair. Deviance is usually defined as twice the loglikelihood ratio. Parameters: endog : array-like The endogenous response variable mu : array-like The inverse of the link function at the linear predicted values. scale : float, optional An optional scale argument Returns: Deviance : array The value of deviance function defined below. Notes Deviance is defined

statsmodels.discrete.discrete_model.MNLogit.cov_params_func_l1 MNLogit.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.moment ExpTransf_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.