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()

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

statsmodels.stats.sandwich_covariance.cov_nw_groupsum statsmodels.stats.sandwich_covariance.cov_nw_groupsum(results, nlags, time, weights_func=, use_correction=0) [source] Driscoll and Kraay Panel robust covariance matrix Robust covariance matrix for panel data of Driscoll and Kraay. Assumes we have a panel of time series where the time index is available. The time index is assumed to represent equal spaced periods. At least one observation per period is required. Parameters: results : resu