Nonparametric Methods nonparametric

Nonparametric Methods nonparametric This section collects various methods in nonparametric statistics. This includes kernel density estimation for univariate and multivariate data, kernel regression and locally weighted scatterplot smoothing (lowess). sandbox.nonparametric contains additional functions that are work in progress or don?t have unit tests yet. We are planning to include here nonparametric density estimators, especially based on kernel or orthogonal polynomials, smoothers, and tool

NonlinearIVGMM.start_weights()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.start_weights NonlinearIVGMM.start_weights(inv=True)

NonlinearIVGMM.score_cu()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.score_cu NonlinearIVGMM.score_cu(params, epsilon=None, centered=True)

NonlinearIVGMM.score()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.score NonlinearIVGMM.score(params, weights, **kwds) [source]

NonlinearIVGMM.predict()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.predict NonlinearIVGMM.predict(params, exog=None) [source]

NonlinearIVGMM.momcond_mean()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.momcond_mean NonlinearIVGMM.momcond_mean(params) mean of moment conditions,

NonlinearIVGMM.momcond()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.momcond NonlinearIVGMM.momcond(params)

NonlinearIVGMM.jac_func()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.jac_func NonlinearIVGMM.jac_func(params, weights, args=None, centered=True, epsilon=None) [source]

NonlinearIVGMM.jac_error()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.jac_error NonlinearIVGMM.jac_error(params, weights, args=None, centered=True, epsilon=None) [source]

NonlinearIVGMM.gradient_momcond()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.gradient_momcond NonlinearIVGMM.gradient_momcond(params, epsilon=0.0001, centered=True) gradient of moment conditions Parameters: params : ndarray parameter at which the moment conditions are evaluated epsilon : float stepsize for finite difference calculation centered : bool This refers to the finite difference calculation. If centered is true, then the centered finite difference calculation is used. Otherwise the one-sided forward dif