Robust Linear Models¶

Robust linear models with support for the M-estimators listed under Norms.

See Module Reference for commands and arguments.

Examples¶

# Load modules and data
In [1]: import statsmodels.api as sm

In [2]: data = sm.datasets.stackloss.load()

In [3]: data.exog = sm.add_constant(data.exog)

# Fit model and print summary
In [4]: rlm_model = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())

In [5]: rlm_results = rlm_model.fit()

In [6]: print(rlm_results.params)
const       -41.026498
AIRFLOW       0.829384
WATERTEMP     0.926066
ACIDCONC     -0.127847
dtype: float64

Detailed examples can be found here:

• Robust Models 1

• Robust Models 2

Technical Documentation¶

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¶

Model Classes¶

Model Results¶

Norms¶

Scale¶