statsmodels.robust.robust_linear_model.RLM class statsmodels.robust.robust_linear_model.RLM(endog, exog, M=, missing='none', **kwargs) [source] Robust Linear Models Estimate a robust linear model via iteratively reweighted least squares given a robust criterion estimator. Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not

statsmodels.robust.norms.TukeyBiweight class statsmodels.robust.norms.TukeyBiweight(c=4.685) [source] Tukey?s biweight function for M-estimation. Parameters: c : float, optional The tuning constant for Tukey?s Biweight. The default value is c = 4.685. Notes Tukey?s biweight is sometime?s called bisquare. Methods psi(z) The psi function for Tukey?s biweight estimator psi_deriv(z) The derivative of Tukey?s biweight psi function rho(z) The robust criterion function for Tukey?s biweight e

statsmodels.robust.norms.TrimmedMean class statsmodels.robust.norms.TrimmedMean(c=2.0) [source] Trimmed mean function for M-estimation. Parameters: c : float, optional The tuning constant for Ramsay?s Ea function. The default value is 2.0. See also statsmodels.robust.norms.RobustNorm Methods psi(z) The psi function for least trimmed mean psi_deriv(z) The derivative of least trimmed mean psi function rho(z) The robust criterion function for least trimmed mean. weights(z) Least trimm

statsmodels.robust.norms.RobustNorm class statsmodels.robust.norms.RobustNorm [source] The parent class for the norms used for robust regression. Lays out the methods expected of the robust norms to be used by statsmodels.RLM. Parameters: None : : Some subclasses have optional tuning constants. See also statsmodels.rlm, and Notes Currently only M-estimators are available. References PJ Huber. ?Robust Statistics? John Wiley and Sons, Inc., New York, 1981. DC Montgomery, EA Peck. ?Introd

statsmodels.robust.norms.RamsayE class statsmodels.robust.norms.RamsayE(a=0.3) [source] Ramsay?s Ea for M estimation. Parameters: a : float, optional The tuning constant for Ramsay?s Ea function. The default value is 0.3. See also statsmodels.robust.norms.RobustNorm Methods psi(z) The psi function for Ramsay?s Ea estimator psi_deriv(z) The derivative of Ramsay?s Ea psi function. rho(z) The robust criterion function for Ramsay?s Ea. weights(z) Ramsay?s Ea weighting function for the

statsmodels.robust.norms.LeastSquares class statsmodels.robust.norms.LeastSquares [source] Least squares rho for M-estimation and its derived functions. See also statsmodels.robust.norms.RobustNorm Methods psi(z) The psi function for the least squares estimator psi_deriv(z) The derivative of the least squares psi function. rho(z) The least squares estimator rho function weights(z) The least squares estimator weighting function for the IRLS algorithm.

statsmodels.robust.norms.HuberT class statsmodels.robust.norms.HuberT(t=1.345) [source] Huber?s T for M estimation. Parameters: t : float, optional The tuning constant for Huber?s t function. The default value is 1.345. See also statsmodels.robust.norms.RobustNorm Methods psi(z) The psi function for Huber?s t estimator psi_deriv(z) The derivative of Huber?s t psi function rho(z) The robust criterion function for Huber?s t. weights(z) Huber?s t weighting function for the IRLS algori

statsmodels.robust.norms.Hampel class statsmodels.robust.norms.Hampel(a=2.0, b=4.0, c=8.0) [source] Hampel function for M-estimation. Parameters: a : float, optional b : float, optional c : float, optional The tuning constants for Hampel?s function. The default values are a,b,c = 2, 4, 8. See also statsmodels.robust.norms.RobustNorm Methods psi(z) The psi function for Hampel?s estimator psi_deriv(z) rho(z) The robust criterion function for Hampel?s estimator weights(z) Hampel weig

statsmodels.robust.norms.estimate_location statsmodels.robust.norms.estimate_location(a, scale, norm=None, axis=0, initial=None, maxiter=30, tol=1e-06) [source] M-estimator of location using self.norm and a current estimator of scale. This iteratively finds a solution to norm.psi((a-mu)/scale).sum() == 0 Parameters: a : array Array over which the location parameter is to be estimated scale : array Scale parameter to be used in M-estimator norm : RobustNorm, optional Robust norm used in

statsmodels.robust.norms.AndrewWave class statsmodels.robust.norms.AndrewWave(a=1.339) [source] Andrew?s wave for M estimation. Parameters: a : float, optional The tuning constant for Andrew?s Wave function. The default value is 1.339. See also statsmodels.robust.norms.RobustNorm Methods psi(z) The psi function for Andrew?s wave psi_deriv(z) The derivative of Andrew?s wave psi function rho(z) The robust criterion function for Andrew?s wave. weights(z) Andrew?s wave weighting functi