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# Robust Linear Models

## Introduction

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

### 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,

## Examples

    import scikits.statsmodels as sm
    date = sm.datasets.stackloss.Load()
    data.exog = sm.add_constant(data.exog)
    rlm_model = models.RLM(data.endog, data.exog, M=models.robust.norms.HuberT())
    rlm_results = rlm_model.fit()
    print rlm_results.params

see also the examples and the tests folders

## Module Reference

### Model and Result Classes

`RLM`(endog, exog[, M]) | Robust Linear Models
`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]) | 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]) | The standardized Median Absolute Deviation along given axis of an array.

## Technical Documentation

- Weight Functions

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