statsmodels.discrete.discrete_model.ProbitResults.summary ProbitResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors 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 Returns

statsmodels.sandbox.tsa.fftarma.ArmaFft.pacf ArmaFft.pacf(nobs=None) partial 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 pacf Returns: pacf : array partial autocorrelation of ARMA process given by ar, ma Notes solves yule-walker

statsmodels.sandbox.tsa.fftarma.ArmaFft.filter2 ArmaFft.filter2(x, pad=0) [source] filter a time series using fftconvolve3 with ARMA filter padding of x currently works only if x is 1d in example it produces same observations at beginning as lfilter even without padding. TODO: this returns 1 additional observation at the end

statsmodels.sandbox.tsa.fftarma.ArmaFft.from_coeffs classmethod ArmaFft.from_coeffs(arcoefs, macoefs, nobs=100) Create ArmaProcess instance from coefficients of the lag-polynomials Parameters: arcoefs : array-like Coefficient for autoregressive lag polynomial, not including zero lag. The sign is inverted to conform to the usual time series representation of an ARMA process in statistics. See the class docstring for more information. macoefs : array-like Coefficient for moving-average lag

statsmodels.robust.norms.LeastSquares class statsmodels.robust.norms.LeastSquares [source] Least squares rho for M-estimation and its derived functions. See also statsmodels.robust.norms.RobustNorm Methods psi(z) The psi function for the least squares estimator psi_deriv(z) The derivative of the least squares psi function. rho(z) The least squares estimator rho function weights(z) The least squares estimator weighting function for the IRLS algorithm.

statsmodels.tsa.ar_model.ARResults.fpe static ARResults.fpe() [source]

statsmodels.stats.proportion.proportions_chisquare_allpairs statsmodels.stats.proportion.proportions_chisquare_allpairs(count, nobs, multitest_method='hs') [source] chisquare test of proportions for all pairs of k samples Performs a chisquare test for proportions for all pairwise comparisons. The alternative is two-sided Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. prop : float, optional The proba

statsmodels.tsa.varma_process.VarmaPoly.stacksquare VarmaPoly.stacksquare(a=None, name='ar', orientation='vertical') [source] stack lagpolynomial vertically in 2d square array with eye

statsmodels.genmod.families.links.inverse_squared.deriv2 inverse_squared.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.regression.quantile_regression.QuantRegResults.uncentered_tss static QuantRegResults.uncentered_tss() [source]