statsmodels.regression.quantile_regression.QuantRegResults.HC1_se static QuantRegResults.HC1_se() [source]

statsmodels.genmod.families.links.probit.inverse probit.inverse(z) The inverse of the CDF link Parameters: z : array-like The value of the inverse of the link function at p Returns: p : array Mean probabilities. The value of the inverse of CDF link of z Notes g^(-1)(z) = dbn.cdf(z)

statsmodels.discrete.discrete_model.MNLogit.loglike_and_score MNLogit.loglike_and_score(params) [source] Returns log likelihood and score, efficiently reusing calculations. Note that both of these returned quantities will need to be negated before being minimized by the maximum likelihood fitting machinery.

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

statsmodels.miscmodels.count.PoissonGMLE.predict_distribution PoissonGMLE.predict_distribution(exog) [source] return frozen scipy.stats distribution with mu at estimated prediction

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

statsmodels.tsa.vector_ar.var_model.VARResults.summary VARResults.summary() [source] Compute console output summary of estimates Returns: summary : VARSummary

statsmodels.regression.mixed_linear_model.MixedLMResults.llf static MixedLMResults.llf()

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

statsmodels.discrete.discrete_model.NegativeBinomial.cdf NegativeBinomial.cdf(X) The cumulative distribution function of the model.