.. currentmodule:: statsmodels.robust .. _rlm: Robust Linear Models ==================== Robust linear models with support for the M-estimators listed under `Norms`_. See `Module Reference`_ for commands and arguments. Examples -------- .. ipython:: python # Load modules and data import statsmodels.api as sm data = sm.datasets.stackloss.load() data.exog = sm.add_constant(data.exog) # Fit model and print summary rlm_model = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()) rlm_results = rlm_model.fit() print(rlm_results.params) Detailed examples can be found here: * `Robust Models 1 `_ * `Robust Models 2 `_ Technical Documentation ----------------------- .. toctree:: :maxdepth: 1 rlm_techn1 References ^^^^^^^^^^ * PJ Huber. ‘Robust Statistics’ John Wiley and Sons, Inc., New York. 1981. * PJ Huber. 1973, ‘The 1972 Wald Memorial Lectures: Robust Regression: Asymptotics, Conjectures, and Monte Carlo.’ The Annals of Statistics, 1.5, 799-821. * R Venables, B Ripley. ‘Modern Applied Statistics in S’ Springer, New York, * C Croux, PJ Rousseeuw, 'Time-efficient algorithms for two highly robust estimators of scale' Computational statistics. Physica, Heidelberg, 1992. Module Reference ---------------- .. module:: statsmodels.robust Model Classes ^^^^^^^^^^^^^ .. module:: statsmodels.robust.robust_linear_model .. currentmodule:: statsmodels.robust.robust_linear_model .. autosummary:: :toctree: generated/ RLM Model Results ^^^^^^^^^^^^^ .. autosummary:: :toctree: generated/ RLMResults .. _norms: Norms ^^^^^ .. module:: statsmodels.robust.norms .. currentmodule:: statsmodels.robust.norms .. autosummary:: :toctree: generated/ AndrewWave Hampel HuberT LeastSquares MQuantileNorm RamsayE RobustNorm TrimmedMean TukeyBiweight estimate_location Scale ^^^^^ .. module:: statsmodels.robust.scale .. currentmodule:: statsmodels.robust.scale .. autosummary:: :toctree: generated/ Huber HuberScale mad hubers_scale iqr qn_scale