statsmodels.tsa.arima_process.arma_acf statsmodels.tsa.arima_process.arma_acf(ar, ma, nobs=10) [source] theoretical autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned acf Returns: acf : array autocorrelation of ARMA process given by ar, m

statsmodels.discrete.discrete_model.CountResults.cov_params CountResults.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-like,

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

statsmodels.nonparametric.kernel_density.EstimatorSettings class statsmodels.nonparametric.kernel_density.EstimatorSettings(efficient=False, randomize=False, n_res=25, n_sub=50, return_median=True, return_only_bw=False, n_jobs=-1) Object to specify settings for density estimation or regression. EstimatorSettings has several proporties related to how bandwidth estimation for the KDEMultivariate, KDEMultivariateConditional, KernelReg and CensoredKernelReg classes behaves. Parameters: efficien

statsmodels.tools.eval_measures.medianabs statsmodels.tools.eval_measures.medianabs(x1, x2, axis=0) [source] median absolute error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: medianabs : ndarray or float median absolute difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to

statsmodels.genmod.families.family.Binomial.loglike Binomial.loglike(endog, mu, scale=1.0) [source] Loglikelihood function for Binomial exponential family distribution. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes If endog is binary: llf = scale*sum(endog*log(mu/(1-

statsmodels.nonparametric.kernel_regression.KernelCensoredReg.censored KernelCensoredReg.censored(censor_val) [source]

statsmodels.regression.linear_model.RegressionResults.wresid static RegressionResults.wresid() [source]

statsmodels.nonparametric.bandwidths.bw_silverman statsmodels.nonparametric.bandwidths.bw_silverman(x, kernel=None) [source] Silverman?s Rule of Thumb Parameters: x : array-like Array for which to get the bandwidth kernel : CustomKernel object Unused Returns: bw : float The estimate of the bandwidth Notes Returns .9 * A * n ** (-1/5.) where A = min(std(x, ddof=1), IQR/1.349) IQR = np.subtract.reduce(np.percentile(x, [75,25])) References Silverman, B.W. (1986) Density Estimation.

statsmodels.genmod.generalized_estimating_equations.GEEResults.plot_isotropic_dependence GEEResults.plot_isotropic_dependence(ax=None, xpoints=10, min_n=50) [source] Create a plot of the pairwise products of within-group residuals against the corresponding time differences. This plot can be used to assess the possible form of an isotropic covariance structure. Parameters: ax : Matplotlib axes instance An axes on which to draw the graph. If None, new figure and axes objects are created xpo