statsmodels.tsa.vector_ar.var_model.FEVD.cov FEVD.cov() [source] Compute asymptotic standard errors

statsmodels.sandbox.distributions.transformed.lognormalg statsmodels.sandbox.distributions.transformed.lognormalg = a class for non-linear monotonic transformation of a continuous random variable

statsmodels.regression.quantile_regression.QuantRegResults.pvalues static QuantRegResults.pvalues()

statsmodels.graphics.gofplots.ProbPlot class statsmodels.graphics.gofplots.ProbPlot(data, dist=, fit=False, distargs=(), a=0, loc=0, scale=1) [source] Class for convenient construction of Q-Q, P-P, and probability plots. Can take arguments specifying the parameters for dist or fit them automatically. (See fit under kwargs.) Parameters: data : array-like 1d data array dist : A scipy.stats or statsmodels distribution Compare x against dist. The default is scipy.stats.distributions.norm (a

statsmodels.graphics.tsaplots.plot_pacf statsmodels.graphics.tsaplots.plot_pacf(x, ax=None, lags=None, alpha=0.05, method='ywm', use_vlines=True, **kwargs) [source] Plot the partial autocorrelation function Plots lags on the horizontal and the correlations on vertical axis. Parameters: x : array_like Array of time-series values ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. lags : array_like, optional Arra

statsmodels.sandbox.stats.multicomp.StepDown.get_crit StepDown.get_crit(alpha) [source]

statsmodels.genmod.cov_struct.Independence.initialize Independence.initialize(model) Called by GEE, used by implementations that need additional setup prior to running fit. Parameters: model : GEE class A reference to the parent GEE class instance.

statsmodels.genmod.cov_struct.Exchangeable.update Exchangeable.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.genmod.families.links.Link.deriv2 Link.deriv2(p) [source] Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional class statsmodels.nonparametric.kernel_density.KDEMultivariateConditional(endog, exog, dep_type, indep_type, bw, defaults=) [source] Conditional multivariate kernel density estimator. Calculates P(Y_1,Y_2,...Y_n | X_1,X_2...X_m) = P(X_1, X_2,...X_n, Y_1, Y_2,..., Y_m)/P(X_1, X_2,..., X_m). The conditional density is by definition the ratio of the two densities, see [R8]. Parameters: endog: list of ndarrays or 2-D ndarray :