statsmodels.sandbox.tsa.fftarma.ArmaFft.acf2spdfreq ArmaFft.acf2spdfreq(acovf, nfreq=100, w=None) [source] not really a method just for comparison, not efficient for large n or long acf this is also similarly use in tsa.stattools.periodogram with window

statsmodels.sandbox.tsa.fftarma.ArmaFft.periodogram ArmaFft.periodogram(nobs=None) periodogram for ARMA process given by lag-polynomials ar and ma Parameters: ar : array_like autoregressive lag-polynomial with leading 1 and lhs sign ma : array_like moving average lag-polynomial with leading 1 worN : {None, int}, optional option for scipy.signal.freqz (read ?w or N?) If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that many frequencie

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

statsmodels.sandbox.regression.gmm.IVRegressionResults.cov_HC1 static IVRegressionResults.cov_HC1() See statsmodels.RegressionResults

statsmodels.genmod.generalized_linear_model.GLMResults.resid_pearson static GLMResults.resid_pearson() [source]

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.moment TransfTwo_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.discrete.discrete_model.CountResults.initialize CountResults.initialize(model, params, **kwd)

statsmodels.tsa.arima_model.ARMAResults.cov_params ARMAResults.cov_params() [source]

Distributions This section collects various additional functions and methods for statistical distributions. Empirical Distributions ECDF(x[, side]) Return the Empirical CDF of an array as a step function. StepFunction(x, y[, ival, sorted, side]) A basic step function. Distribution Extras Skew Distributions SkewNorm_gen() univariate Skew-Normal distribution of Azzalini SkewNorm2_gen([momtype, a, b, xtol, ...]) univariate Skew-Normal distribution of Azzalini ACSkewT_gen() univariate Skew-

statsmodels.sandbox.regression.try_catdata.labelmeanfilter statsmodels.sandbox.regression.try_catdata.labelmeanfilter(y, x) [source]