statsmodels.genmod.families.family.NegativeBinomial.predict NegativeBinomial.predict(mu) Linear predictors based on given mu values. Parameters: mu : array The mean response variables Returns: lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu.

statsmodels.regression.linear_model.GLS.whiten GLS.whiten(X) [source] GLS whiten method. Parameters: X : array-like Data to be whitened. Returns: np.dot(cholsigmainv,X) : See also regression.GLS

statsmodels.regression.quantile_regression.QuantReg.hessian QuantReg.hessian(params) The Hessian matrix of the model

statsmodels.regression.linear_model.OLSResults.initialize OLSResults.initialize(model, params, **kwd)

statsmodels.graphics.functional.banddepth statsmodels.graphics.functional.banddepth(data, method='MBD') [source] Calculate the band depth for a set of functional curves. Band depth is an order statistic for functional data (see fboxplot), with a higher band depth indicating larger ?centrality?. In analog to scalar data, the functional curve with highest band depth is called the median curve, and the band made up from the first N/2 of N curves is the 50% central region. Parameters: data : nd

Kernel Density Estimation Link to Notebook GitHub In [1]: import numpy as np from scipy import stats import statsmodels.api as sm import matplotlib.pyplot as plt from statsmodels.distributions.mixture_rvs import mixture_rvs A univariate example. In [2]: np.random.seed(12345) In [3]: obs_dist1 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.norm], kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5))) In [4]: kde = sm.non

statsmodels.stats.outliers_influence.OLSInfluence.summary_frame OLSInfluence.summary_frame() [source] Creates a DataFrame with all available influence results. Returns: frame : DataFrame A DataFrame with all results. Notes The resultant DataFrame contains six variables in addition to the DFBETAS. These are: cooks_d : Cook?s Distance defined in Influence.cooks_distance standard_resid : Standardized residuals defined in Influence.resid_studentized_internal hat_diag : The diagonal of the

statsmodels.sandbox.regression.gmm.GMMResults.q static GMMResults.q() [source]

statsmodels.sandbox.regression.gmm.IV2SLS.initialize IV2SLS.initialize() [source]

statsmodels.discrete.discrete_model.ProbitResults.aic static ProbitResults.aic()