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

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

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

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

statsmodels.discrete.discrete_model.MultinomialResults.llf static MultinomialResults.llf()

statsmodels.tools.tools.rank statsmodels.tools.tools.rank(X, cond=1e-12) [source] Return the rank of a matrix X based on its generalized inverse, not the SVD.

statsmodels.miscmodels.tmodel.TLinearModel.score TLinearModel.score(params) Gradient of log-likelihood evaluated at params

statsmodels.discrete.discrete_model.Logit.cov_params_func_l1 Logit.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.