statsmodels.sandbox.stats.runs.Runs.runs_test Runs.runs_test(correction=True) [source] basic version of runs test Parameters: correction: bool : Following the SAS manual, for samplesize below 50, the test statistic is corrected by 0.5. This can be turned off with correction=False, and was included to match R, tseries, which does not use any correction. pvalue based on normal distribution, with integer correction :

statsmodels.robust.norms.RobustNorm.rho RobustNorm.rho(z) [source] The robust criterion estimator function. Abstract method: -2 loglike used in M-estimator

statsmodels.robust.norms.RobustNorm.psi_deriv RobustNorm.psi_deriv(z) [source] Deriative of psi. Used to obtain robust covariance matrix. See statsmodels.rlm for more information. Abstract method: psi_derive = psi?

statsmodels.robust.scale.stand_mad statsmodels.robust.scale.stand_mad(a, c=0.67448975019608171, axis=0) [source]

statsmodels.robust.norms.RobustNorm.psi RobustNorm.psi(z) [source] Derivative of rho. Sometimes referred to as the influence function. Abstract method: psi = rho?

statsmodels.robust.scale.HuberScale class statsmodels.robust.scale.HuberScale(d=2.5, tol=1e-08, maxiter=30) [source] Huber?s scaling for fitting robust linear models. Huber?s scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the Huber class. Parameters: d : float, optional d is the tuning constant for Huber?s scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations

statsmodels.robust.scale.hubers_scale statsmodels.robust.scale.hubers_scale = Huber?s scaling for fitting robust linear models. Huber?s scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the Huber class. Parameters: d : float, optional d is the tuning constant for Huber?s scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations. The default is 30. Notes Huber?s s

statsmodels.robust.scale.mad statsmodels.robust.scale.mad(a, c=0.67448975019608171, axis=0, center=) [source] The Median Absolute Deviation along given axis of an array Parameters: a : array-like Input array. c : float, optional The normalization constant. Defined as scipy.stats.norm.ppf(3/4.), which is approximately .6745. axis : int, optional The defaul is 0. Can also be None. center : callable or float If a callable is provided, such as the default np.median then it is expected to

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.robust.scale.Huber class statsmodels.robust.scale.Huber(c=1.5, tol=1e-08, maxiter=30, norm=None) [source] Huber?s proposal 2 for estimating location and scale jointly. Parameters: c : float, optional Threshold used in threshold for chi=psi**2. Default value is 1.5. tol : float, optional Tolerance for convergence. Default value is 1e-08. maxiter : int, optional0 Maximum number of iterations. Default value is 30. norm : statsmodels.robust.norms.RobustNorm, optional A robust