statsmodels.sandbox.distributions.transformed.SquareFunc.derivplus SquareFunc.derivplus(x) [source]

statsmodels.sandbox.regression.gmm.LinearIVGMM.gmmobjective_cu LinearIVGMM.gmmobjective_cu(params, weights_method='cov', wargs=()) objective function for continuously updating GMM minimization Parameters: params : array parameter values at which objective is evaluated Returns: jval : float value of objective function

statsmodels.duration.hazard_regression.PHReg.fit_regularized PHReg.fit_regularized(method='coord_descent', maxiter=100, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-07, zero_tol=1e-08, **kwargs) [source] Return a regularized fit to a linear regression model. Parameters: method : : Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar

statsmodels.regression.linear_model.RegressionResults.rsquared_adj static RegressionResults.rsquared_adj() [source]

statsmodels.sandbox.regression.gmm.IVGMMResults.get_bse IVGMMResults.get_bse(**kwds) standard error of the parameter estimates with options Parameters: kwds : optional keywords options for calculating cov_params Returns: bse : ndarray estimated standard error of parameter estimates

statsmodels.robust.robust_linear_model.RLMResults.t_test RLMResults.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 a

statsmodels.discrete.discrete_model.BinaryResults.conf_int BinaryResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interva

statsmodels.sandbox.distributions.extras.ACSkewT_gen.logsf ACSkewT_gen.logsf(x, *args, **kwds) Log of the survival function of the given RV. Returns the log of the ?survival function,? defined as (1 - cdf), evaluated at x. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional sca

statsmodels.nonparametric.bandwidths.bw_scott statsmodels.nonparametric.bandwidths.bw_scott(x, kernel=None) [source] Scott?s Rule of Thumb Parameters: x : array-like Array for which to get the bandwidth kernel : CustomKernel object Unused Returns: bw : float The estimate of the bandwidth Notes Returns 1.059 * A * n ** (-1/5.) where A = min(std(x, ddof=1), IQR/1.349) IQR = np.subtract.reduce(np.percentile(x, [75,25])) References Scott, D.W. (1992) Multivariate Density Estimation: T

statsmodels.discrete.discrete_model.Poisson.fit_constrained Poisson.fit_constrained(constraints, start_params=None, **fit_kwds) [source] fit the model subject to linear equality constraints The constraints are of the form R params = q where R is the constraint_matrix and q is the vector of constraint_values. The estimation creates a new model with transformed design matrix, exog, and converts the results back to the original parameterization. Parameters: constraints : formula expression or