statsmodels.robust.robust_linear_model.RLM.hessian RLM.hessian(params) The Hessian matrix of the model

statsmodels.discrete.discrete_model.ProbitResults.initialize ProbitResults.initialize(model, params, **kwd)

statsmodels.genmod.cov_struct.GlobalOddsRatio class statsmodels.genmod.cov_struct.GlobalOddsRatio(endog_type) [source] Estimate the global odds ratio for a GEE with ordinal or nominal data. Notes The following data structures are calculated in the class: ?ibd? is a list whose i^th element ibd[i] is a sequence of integer pairs (a,b), where endog_li[i][a:b] is the subvector of binary indicators derived from the same ordinal value. cpp is a dictionary where cpp[group] is a map from cut-point pa

statsmodels.regression.linear_model.OLSResults.centered_tss static OLSResults.centered_tss()

statsmodels.tsa.stattools.arma_order_select_ic statsmodels.tsa.stattools.arma_order_select_ic(y, max_ar=4, max_ma=2, ic='bic', trend='c', model_kw={}, fit_kw={}) [source] Returns information criteria for many ARMA models Parameters: y : array-like Time-series data max_ar : int Maximum number of AR lags to use. Default 4. max_ma : int Maximum number of MA lags to use. Default 2. ic : str, list Information criteria to report. Either a single string or a list of different criteria is po

statsmodels.regression.quantile_regression.QuantRegResults.scale QuantRegResults.scale() [source]

statsmodels.regression.linear_model.RegressionResults.summary RegressionResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns

statsmodels.discrete.discrete_model.NegativeBinomialResults.llr static NegativeBinomialResults.llr()

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-