statsmodels.robust.resistant_linear_model.RLMDetSMM

class statsmodels.robust.resistant_linear_model.RLMDetSMM(endog, exog, norm=None, efficiency=0.95, breakdown_point=0.5, col_indices=None, include_endog=False)

MM-estimator with S-estimator starting values.

Parameters:

endogarray_like

Dependent, endogenous variable.

exogarray_like

Independent, exogenous regressor variables.

normstatsmodels.robust.norms.RobustNorm, optional

Redescending robust norm used for S- and MM-estimation. Default is TukeyBiweight.

efficiencyfloat in (0, 1), optional

Asymptotic efficiency of the MM-estimator (used in second stage).

breakdown_pointfloat in (0, 0.5), optional

Breakdown point of the preliminary S-estimator.

col_indicesarray_like[int], optional

Index of columns of exog to use in the mahalanobis distance computation for the starting sets of the S-estimator. If None (default), all exog columns except the first (constant) column are used. Todo: will change when we autodetect the constant column.

include_endogbool, optional

If true, then the endog variable is combined with the exog variables to compute the mahalanobis distances for the starting sets of the S-estimator.

Attributes:

endog_names

Names of endogenous variables

exog_names

Names of exogenous variables

Methods

fit([h, scale_binding, start])	Estimate the model
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe
predict(params[, exog])	After a model has been fit, predict returns the fitted values

Methods

fit([h, scale_binding, start])	Estimate the model
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe
predict(params[, exog])	After a model has been fit, predict returns the fitted values

Properties

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables