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.. 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 <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
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