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 <examples/notebooks/generated/robust_models_0.ipynb>_
Robust Models 2 <examples/notebooks/generated/robust_models_1.ipynb>_

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