IRAnalysis.cov()

statsmodels.tsa.vector_ar.irf.IRAnalysis.cov IRAnalysis.cov(orth=False) [source] Compute asymptotic standard errors for impulse response coefficients Notes Lutkepohl eq 3.7.5

ExpTransf_gen.nnlf()

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.nnlf ExpTransf_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

Probit.fit()

statsmodels.discrete.discrete_model.Probit.fit Probit.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional Th

static RegressionResults.cov_HC2()

statsmodels.regression.linear_model.RegressionResults.cov_HC2 static RegressionResults.cov_HC2() [source] See statsmodels.RegressionResults

stats.diagnostic.unitroot_adf()

statsmodels.stats.diagnostic.unitroot_adf statsmodels.stats.diagnostic.unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False)

static NegativeBinomialResults.prsquared()

statsmodels.discrete.discrete_model.NegativeBinomialResults.prsquared static NegativeBinomialResults.prsquared()

static ProbitResults.pvalues()

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

CountModel.cov_params_func_l1()

statsmodels.discrete.discrete_model.CountModel.cov_params_func_l1 CountModel.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

static NegativeBinomialResults.aic()

statsmodels.discrete.discrete_model.NegativeBinomialResults.aic static NegativeBinomialResults.aic() [source]

ARMAResults.load()

statsmodels.tsa.arima_model.ARMAResults.load classmethod ARMAResults.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 :