Robust linear models with support for the M-estimators listed under norms.
PJ Huber. ‘Robust Statistics’ John Wiley and Sons, Inc., New York. 1981.
import scikits.statsmodels as sm
date = sm.datasets.stackloss.Load()
data.exog = sm.add_constant(data.exog)
rlm_model = models.RLM(data.endog, data.exog, M=models.robust.norms.HuberT())
rlm_results = rlm_model.fit()
print rlm_results.params
see also the examples and the tests folders
| RLM(endog, exog[, M]) | Robust Linear Models |
| RLMResults(model, params, ...) | Class to contain RLM results |
| AndrewWave([a]) | Andrew’s wave for M estimation. |
| Hampel([a, b, c]) | Hampel function for M-estimation. |
| HuberT([t]) | Huber’s T for M estimation. |
| LeastSquares | Least squares rho for M-estimation and its derived functions. |
| RamsayE([a]) | Ramsay’s Ea for M estimation. |
| RobustNorm | The parent class for the norms used for robust regression. |
| TrimmedMean([c]) | Trimmed mean function for M-estimation. |
| TukeyBiweight([c]) | Tukey’s biweight function for M-estimation. |
| estimate_location(a, scale[, norm, axis, ...]) | M-estimator of location using self.norm and a current estimator of scale. |
| Huber([c, tol, maxiter, norm]) | Huber’s proposal 2 for estimating location and scale jointly. |
| HuberScale([d, tol, maxiter]) | Huber’s scaling for fitting robust linear models. |
| mad(a[, c, axis]) | The Median Absolute Deviation along given axis of an array |
| huber | Huber’s proposal 2 for estimating location and scale jointly. |
| hubers_scale | Huber’s scaling for fitting robust linear models. |
| stand_mad(a[, c, axis]) | The standardized Median Absolute Deviation along given axis of an array. |