Robust Linear Models
Robust linear models with support for the M-estimators listed under Norms.
See Module Reference for commands and arguments.
Examples
1 2 3 4 5 6 7 8 9 | # Load modules and dataimport statsmodels.api as smdata = sm.datasets.stackloss.load()data.exog = sm.add_constant(data.exog)# Fit model and print summaryrlm_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:
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,
Module Reference
Model Classes
RLM(endog, exog[, M, missing]) | Robust Linear Models |
Model Results
RLMResults(model, params, ...) | Class to contain RLM results |
Norms
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. |
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, center]) | 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]) |
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