statsmodels.regression.quantile_regression.QuantRegResults.mse static QuantRegResults.mse() [source]

statsmodels.discrete.discrete_model.DiscreteResults.cov_params DiscreteResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-

statsmodels.discrete.discrete_model.NegativeBinomial.fit_regularized NegativeBinomial.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) [source]

statsmodels.sandbox.stats.multicomp.StepDown.run StepDown.run(alpha) [source] main function to run the test, could be done in __call__ instead this could have all the initialization code

statsmodels.sandbox.stats.multicomp.StepDown.check_set StepDown.check_set(indices) [source] check whether pairwise distances of indices satisfy condition

statsmodels.tsa.arima_model.ARIMA class statsmodels.tsa.arima_model.ARIMA(endog, order, exog=None, dates=None, freq=None, missing='none') [source] Autoregressive Integrated Moving Average ARIMA(p,d,q) Model Parameters: endog : array-like The endogenous variable. order : iterable The (p,d,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should not include a constant or

statsmodels.tsa.arima_model.ARMAResults.aic static ARMAResults.aic() [source]

statsmodels.regression.quantile_regression.QuantRegResults.HC0_se static QuantRegResults.HC0_se() [source]

statsmodels.sandbox.regression.gmm.IVGMMResults.initialize IVGMMResults.initialize(model, params, **kwd)

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 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 Linear Models