statsmodels.robust.norms.HuberT.rho HuberT.rho(z) [source] The robust criterion function for Huber?s t. Parameters: z : array-like 1d array Returns: rho : array rho(z) = .5*z**2 for |z| <= t rho(z) = |z|*t - .5*t**2 for |z| > t

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.cdf SkewNorm2_gen.cdf(x, *args, **kwds) Cumulative distribution function of the given RV. Parameters: x : array_like quantiles 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: cdf : ndarray Cumulative distribution fu

statsmodels.sandbox.distributions.extras.NormExpan_gen.moment NormExpan_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.

statsmodels.tools.eval_measures.medianabs statsmodels.tools.eval_measures.medianabs(x1, x2, axis=0) [source] median absolute error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: medianabs : ndarray or float median absolute difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.var SkewNorm2_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.genmod.families.links.inverse_squared.deriv inverse_squared.deriv(p) Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.pdf SkewNorm2_gen.pdf(x, *args, **kwds) Probability density function at x of the given RV. Parameters: x : array_like quantiles 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: pdf : ndarray Probability density funct

statsmodels.sandbox.regression.gmm.IVRegressionResults.mse_resid static IVRegressionResults.mse_resid()

statsmodels.iolib.summary.Summary.add_table_params Summary.add_table_params(res, yname=None, xname=None, alpha=0.05, use_t=True) [source] create and add a table for the parameter estimates Parameters: res : results instance some required information is directly taken from the result instance yname : string or None optional name for the endogenous variable, default is ?y? xname : list of strings or None optional names for the exogenous variables, default is ?var_xx? alpha : float sign

statsmodels.stats.sandwich_covariance.cov_hc1 statsmodels.stats.sandwich_covariance.cov_hc1(results) [source] See statsmodels.RegressionResults