statsmodels.tsa.arima_model.ARIMA.score ARIMA.score(params) Compute the score function at params. Notes This is a numerical approximation.

statsmodels.robust.scale.stand_mad statsmodels.robust.scale.stand_mad(a, c=0.67448975019608171, axis=0) [source]

statsmodels.discrete.discrete_model.CountResults.llnull static CountResults.llnull()

statsmodels.sandbox.tsa.fftarma.ArmaFft.filter ArmaFft.filter(x) [source] filter a timeseries with the ARMA filter padding with zero is missing, in example I needed the padding to get initial conditions identical to direct filter Initial filtered observations differ from filter2 and signal.lfilter, but at end they are the same. See also tsa.filters.fftconvolve

statsmodels.sandbox.regression.gmm.IVRegressionResults.initialize IVRegressionResults.initialize(model, params, **kwd)

statsmodels.regression.quantile_regression.QuantRegResults.tvalues static QuantRegResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.sandbox.tsa.fftarma.ArmaFft.fftar ArmaFft.fftar(n=None) [source] Fourier transform of AR polynomial, zero-padded at end to n Parameters: n : int length of array after zero-padding Returns: fftar : ndarray fft of zero-padded ar polynomial

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

statsmodels.regression.mixed_linear_model.MixedLMResults.initialize MixedLMResults.initialize(model, params, **kwd)

statsmodels.tsa.arima_model.ARMAResults.summary2 ARMAResults.summary2(title=None, alpha=0.05, float_format='%.4f') [source] Experimental summary function for ARIMA Results Parameters: title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format: string : print format for floats in parameters summary Returns: smry : Summary instance This holds the summary table and text,