statsmodels.discrete.discrete_model.BinaryModel.information BinaryModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.distributions.extras.ACSkewT_gen class statsmodels.sandbox.distributions.extras.ACSkewT_gen [source] univariate Skew-T distribution of Azzalini class follows scipy.stats.distributions pattern but with __init__ Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...]) Calculate expected val

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

statsmodels.sandbox.stats.multicomp.GroupsStats.runbasic_old GroupsStats.runbasic_old(useranks=False) [source]

statsmodels.discrete.discrete_model.LogitResults.pvalues static LogitResults.pvalues()

Time Series analysis tsa statsmodels.tsa contains model classes and functions that are useful for time series analysis. This currently includes univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It

statsmodels.tsa.ar_model.ARResults.wald_test ARResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. 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 : A tuple of arrays in

statsmodels.discrete.discrete_model.BinaryModel.pdf BinaryModel.pdf(X) The probability density (mass) function of the model.

statsmodels.tsa.arima_model.ARMA.loglike_kalman ARMA.loglike_kalman(params, set_sigma2=True) [source] Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.

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