statsmodels.robust.norms.LeastSquares.weights LeastSquares.weights(z) [source] The least squares estimator weighting function for the IRLS algorithm. The psi function scaled by the input z Parameters: z : array-like 1d array Returns: weights : array weights(z) = np.ones(z.shape)

statsmodels.robust.norms.LeastSquares.psi_deriv LeastSquares.psi_deriv(z) [source] The derivative of the least squares psi function. Returns: psi_deriv : array ones(z.shape) Notes Used to estimate the robust covariance matrix.

statsmodels.nonparametric.kernel_regression.KernelReg.r_squared KernelReg.r_squared() [source] Returns the R-Squared for the nonparametric regression. Notes For more details see p.45 in [2] The R-Squared is calculated by: where is the mean calculated in fit at the exog points.

statsmodels.robust.norms.LeastSquares.psi LeastSquares.psi(z) [source] The psi function for the least squares estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z

statsmodels.nonparametric.kernel_regression.KernelReg.sig_test KernelReg.sig_test(var_pos, nboot=50, nested_res=25, pivot=False) [source] Significance test for the variables in the regression. Parameters: var_pos: sequence : The position of the variable in exog to be tested. Returns: sig: str : The level of significance: * : at 90% confidence level ** : at 95% confidence level *** : at 99* confidence level ?Not Significant? : if not significant

statsmodels.nonparametric.kernel_regression.KernelReg.fit KernelReg.fit(data_predict=None) [source] Returns the mean and marginal effects at the data_predict points. Parameters: data_predict : array_like, optional Points at which to return the mean and marginal effects. If not given, data_predict == exog. Returns: mean : ndarray The regression result for the mean (i.e. the actual curve). mfx : ndarray The marginal effects, i.e. the partial derivatives of the mean.

statsmodels.nonparametric.kernel_regression.KernelReg.loo_likelihood KernelReg.loo_likelihood()

statsmodels.nonparametric.kernel_regression.KernelCensoredReg.sig_test KernelCensoredReg.sig_test(var_pos, nboot=50, nested_res=25, pivot=False) Significance test for the variables in the regression. Parameters: var_pos: sequence : The position of the variable in exog to be tested. Returns: sig: str : The level of significance: * : at 90% confidence level ** : at 95% confidence level *** : at 99* confidence level ?Not Significant? : if not significant

statsmodels.nonparametric.kernel_regression.KernelReg.cv_loo KernelReg.cv_loo(bw, func) [source] The cross-validation function with leave-one-out estimator. Parameters: bw: array_like : Vector of bandwidth values. func: callable function : Returns the estimator of g(x). Can be either _est_loc_constant (local constant) or _est_loc_linear (local_linear). Returns: L: float : The value of the CV function. Notes Calculates the cross-validation least-squares function. This function is mi

statsmodels.nonparametric.kernel_regression.KernelReg.aic_hurvich KernelReg.aic_hurvich(bw, func=None) [source] Computes the AIC Hurvich criteria for the estimation of the bandwidth. Parameters: bw : str or array_like See the bw parameter of KernelReg for details. Returns: aic : ndarray The AIC Hurvich criteria, one element for each variable. func : None Unused here, needed in signature because it?s used in cv_loo. References See ch.2 in [1] and p.35 in [2].