statsmodels.tsa.arima_model.ARIMA.fit ARIMA.fit(start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, **kwargs) [source] Fits ARIMA(p,d,q) model by exact maximum likelihood via Kalman filter. Parameters: start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to tra

statsmodels.nonparametric.smoothers_lowess.lowess statsmodels.nonparametric.smoothers_lowess.lowess(endog, exog, frac=0.6666666666666666, it=3, delta=0.0, is_sorted=False, missing='drop', return_sorted=True) [source] LOWESS (Locally Weighted Scatterplot Smoothing) A lowess function that outs smoothed estimates of endog at the given exog values from points (exog, endog) Parameters: endog: 1-D numpy array : The y-values of the observed points exog: 1-D numpy array : The x-values of the obs

statsmodels.robust.norms.AndrewWave.psi_deriv AndrewWave.psi_deriv(z) [source] The derivative of Andrew?s wave psi function Notes Used to estimate the robust covariance matrix.

statsmodels.miscmodels.count.PoissonZiGMLE.score PoissonZiGMLE.score(params) Gradient of log-likelihood evaluated at params

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

statsmodels.sandbox.distributions.extras.SkewNorm2_gen class statsmodels.sandbox.distributions.extras.SkewNorm2_gen(momtype=1, a=None, b=None, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, extradoc=None) [source] univariate Skew-Normal distribution of Azzalini class follows scipy.stats.distributions pattern Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds)

statsmodels.robust.robust_linear_model.RLMResults class statsmodels.robust.robust_linear_model.RLMResults(model, params, normalized_cov_params, scale) [source] Class to contain RLM results Returns: **Attributes** : bcov_scaled : array p x p scaled covariance matrix specified in the model fit method. The default is H1. H1 is defined as k**2 * (1/df_resid*sum(M.psi(sresid)**2)*scale**2)/ ((1/nobs*sum(M.psi_deriv(sresid)))**2) * (X.T X)^(-1) where k = 1 + (df_model +1)/nobs * var_psiprime/m**

statsmodels.genmod.cov_struct.CovStruct.summary CovStruct.summary() [source] Returns a text summary of the current estimate of the dependence structure.

statsmodels.tsa.vector_ar.var_model.VARResults.plot_sample_acorr VARResults.plot_sample_acorr(nlags=10, linewidth=8) [source] Plot theoretical autocorrelation function

statsmodels.sandbox.regression.gmm.IVGMMResults.ssr static IVGMMResults.ssr() [source]