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,

statsmodels.regression.linear_model.OLSResults.load classmethod OLSResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.regression.linear_model.RegressionResults.HC2_se static RegressionResults.HC2_se() [source] See statsmodels.RegressionResults

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

statsmodels.tsa.vector_ar.var_model.VARResults.llf static VARResults.llf() [source] Compute VAR(p) loglikelihood

statsmodels.genmod.families.links.CLogLog.deriv CLogLog.deriv(p) [source] Derivative of C-Log-Log transform link function Parameters: p : array-like Mean parameters Returns: g?(p) : array The derivative of the CLogLog transform link function Notes g?(p) = - 1 / (log(p) * p)