statsmodels.sandbox.distributions.extras.SkewNorm_gen.mean SkewNorm_gen.mean(*args, **kwds) Mean of the distribution Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: mean : float the mean of the distribution

statsmodels.tsa.arima_process.ArmaProcess.acf ArmaProcess.acf(nobs=None) [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, ma See also arma_aco

statsmodels.tsa.ar_model.AR.hessian AR.hessian(params) [source] Returns numerical hessian for now.

statsmodels.sandbox.distributions.extras.ACSkewT_gen.fit ACSkewT_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape parameters) an

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

statsmodels.nonparametric.kernel_density.KDEMultivariate.loo_likelihood KDEMultivariate.loo_likelihood(bw, func= at 0x2ac6543ac488>) [source] Returns the leave-one-out likelihood function. The leave-one-out likelihood function for the unconditional KDE. Parameters: bw: array_like : The value for the bandwidth parameter(s). func: callable, optional : Function to transform the likelihood values (before summing); for the log likelihood, use func=np.log. Default is f(x) = x. Notes The l

Weight Functions Andrew?s Wave Hampel 17A Huber?s t Least Squares Ramsay?s Ea Trimmed Mean Tukey?s Biweight

statsmodels.tsa.arima_model.ARMAResults.predict ARMAResults.predict(start=None, end=None, exog=None, dynamic=False) [source] ARMA model in-sample and out-of-sample prediction Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first forecast is start.

statsmodels.stats.proportion.samplesize_confint_proportion statsmodels.stats.proportion.samplesize_confint_proportion(proportion, half_length, alpha=0.05, method='normal') [source] find sample size to get desired confidence interval length Parameters: proportion : float in (0, 1) proportion or quantile half_length : float in (0, 1) desired half length of the confidence interval alpha : float in (0, 1) significance level, default 0.05, coverage of the two-sided interval is (approximatel

statsmodels.genmod.families.links.CLogLog.inverse CLogLog.inverse(z) [source] Inverse of C-Log-Log transform link function Parameters: z : array-like The value of the inverse of the CLogLog link function at p Returns: p : array Mean parameters Notes g^(-1)(z) = 1-exp(-exp(z))