statsmodels.discrete.discrete_model.NegativeBinomial.score_obs NegativeBinomial.score_obs(params) [source]

statsmodels.robust.robust_linear_model.RLM.information RLM.information(params) [source]

Generalized Least Squares Link to Notebook GitHub In [1]: from __future__ import print_function import statsmodels.api as sm import numpy as np from statsmodels.iolib.table import (SimpleTable, default_txt_fmt) The Longley dataset is a time series dataset: In [2]: data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog) print(data.exog[:5]) [[ 1. 83. 234289. 2356. 1590. 107608. 1947. ] [ 1. 88.5 259426.

statsmodels.tsa.ar_model.AR.information AR.information(params) [source] Not Implemented Yet

statsmodels.discrete.discrete_model.Logit.fit Logit.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional The

statsmodels.tsa.vector_ar.var_model.VARProcess.mean VARProcess.mean() [source] Mean of stable process Lutkepohl eq. 2.1.23

statsmodels.regression.quantile_regression.QuantRegResults class statsmodels.regression.quantile_regression.QuantRegResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] Results instance for the QuantReg model Methods HC0_se() HC1_se() HC2_se() HC3_se() aic() bic() bse() centered_tss() compare_f_test(restricted) use F test to test whether restricted model is correct compare_lm_test(restricted[, demean, use_lr

statsmodels.regression.quantile_regression.QuantRegResults.compare_lm_test QuantRegResults.compare_lm_test(restricted, demean=True, use_lr=False) Use Lagrange Multiplier test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. demean : bool Fl

statsmodels.discrete.discrete_model.DiscreteResults.prsquared static DiscreteResults.prsquared() [source]

statsmodels.regression.linear_model.OLSResults.compare_f_test OLSResults.compare_f_test(restricted) use F test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. Returns: f_value : float test statistic, F distributed p_value : float p-val