statsmodels.regression.quantile_regression.QuantRegResults.t_test QuantRegResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple

statsmodels.tsa.arima_process.ArmaProcess.arma2ma ArmaProcess.arma2ma(nobs=None) [source]

statsmodels.sandbox.stats.multicomp.MultiComparison.kruskal MultiComparison.kruskal(pairs=None, multimethod='T') [source] pairwise comparison for kruskal-wallis test This is just a reimplementation of scipy.stats.kruskal and does not yet use a multiple comparison correction.

statsmodels.sandbox.distributions.extras.mvstdnormcdf statsmodels.sandbox.distributions.extras.mvstdnormcdf(lower, upper, corrcoef, **kwds) [source] standardized multivariate normal cumulative distribution function This is a wrapper for scipy.stats.kde.mvn.mvndst which calculates a rectangular integral over a standardized multivariate normal distribution. This function assumes standardized scale, that is the variance in each dimension is one, but correlation can be arbitrary, covariance = co

statsmodels.tsa.arima_model.ARMAResults.llf static ARMAResults.llf() [source]

statsmodels.sandbox.regression.gmm.NonlinearIVGMM class statsmodels.sandbox.regression.gmm.NonlinearIVGMM(endog, exog, instrument, func, **kwds) [source] Class for non-linear instrumental variables estimation wusing GMM The model is assumed to have the following moment condition E[ z * (y - f(X, beta)] = 0 Where y is the dependent endogenous variable, x are the explanatory variables and z are the instruments. Variables in x that are exogenous need also be included in z. f is a nonlinear fun

statsmodels.robust.norms.Hampel.psi_deriv Hampel.psi_deriv(z) [source]

statsmodels.tsa.arima_model.ARMAResults.arfreq static ARMAResults.arfreq() [source] Returns the frequency of the AR roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.

statsmodels.tsa.ar_model.AR.initialize AR.initialize() [source]

statsmodels.miscmodels.count.PoissonZiGMLE.information PoissonZiGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.