statsmodels.nonparametric.kernel_density.KDEMultivariateConditional class statsmodels.nonparametric.kernel_density.KDEMultivariateConditional(endog, exog, dep_type, indep_type, bw, defaults=) [source] Conditional multivariate kernel density estimator. Calculates P(Y_1,Y_2,...Y_n | X_1,X_2...X_m) = P(X_1, X_2,...X_n, Y_1, Y_2,..., Y_m)/P(X_1, X_2,..., X_m). The conditional density is by definition the ratio of the two densities, see [R8]. Parameters: endog: list of ndarrays or 2-D ndarray :

statsmodels.regression.quantile_regression.QuantRegResults.wresid static QuantRegResults.wresid()

statsmodels.regression.linear_model.OLSResults.t_test OLSResults.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 : A tuple of arra

statsmodels.sandbox.distributions.extras.ACSkewT_gen.nnlf ACSkewT_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).

statsmodels.discrete.discrete_model.MultinomialResults.t_test MultinomialResults.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.regression.quantile_regression.QuantRegResults.centered_tss static QuantRegResults.centered_tss() [source]

statsmodels.sandbox.regression.gmm.IVGMM.score IVGMM.score(params, weights, epsilon=None, centered=True)

statsmodels.stats.diagnostic.kstest_normal statsmodels.stats.diagnostic.kstest_normal(x, pvalmethod='approx') Lillifors test for normality, Kolmogorov Smirnov test with estimated mean and variance Parameters: x : array_like, 1d data series, sample pvalmethod : ?approx?, ?table? ?approx? uses the approximation formula of Dalal and Wilkinson, valid for pvalues < 0.1. If the pvalue is larger than 0.1, then the result of table is returned ?table? uses the table from Dalal and Wilkinson, w

statsmodels.regression.quantile_regression.QuantRegResults.cov_HC3 static QuantRegResults.cov_HC3() See statsmodels.RegressionResults

statsmodels.regression.linear_model.WLS.hessian WLS.hessian(params) The Hessian matrix of the model