statsmodels.tsa.stattools.pacf_yw statsmodels.tsa.stattools.pacf_yw(x, nlags=40, method='unbiased') [source] Partial autocorrelation estimated with non-recursive yule_walker Parameters: x : 1d array observations of time series for which pacf is calculated nlags : int largest lag for which pacf is returned method : ?unbiased? (default) or ?mle? method for the autocovariance calculations in yule walker Returns: pacf : 1d array partial autocorrelations, maxlag+1 elements Notes This

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.imse KDEMultivariateConditional.imse(bw) [source] The integrated mean square error for the conditional KDE. Parameters: bw: array_like : The bandwidth parameter(s). Returns: CV: float : The cross-validation objective function. Notes For more details see pp. 156-166 in [R15]. For details on how to handle the mixed variable types see [R16]. The formula for the cross-validation objective function for mixed variable type

statsmodels.genmod.families.family.Gamma.fitted Gamma.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.sandbox.distributions.extras.skewnorm2 statsmodels.sandbox.distributions.extras.skewnorm2 = univariate Skew-Normal distribution of Azzalini class follows scipy.stats.distributions pattern

statsmodels.sandbox.regression.gmm.IVRegressionResults.normalized_cov_params IVRegressionResults.normalized_cov_params()

statsmodels.genmod.generalized_linear_model.GLM.hessian GLM.hessian(params, scale=None, observed=True) [source] Hessian, second derivative of loglikelihood function Parameters: params : ndarray parameter at which Hessian is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by self.scaletype and set in fit. If scale is not None, then it is used as a fixed scale. observed : bool If True, then the observed Hessian is retu

statsmodels.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.genmod.families.links.Power class statsmodels.genmod.families.links.Power(power=1.0) [source] The power transform Parameters: power : float The exponent of the power transform Notes Aliases of Power: inverse = Power(power=-1) sqrt = Power(power=.5) inverse_squared = Power(power=-2.) identity = Power(power=1.) Methods deriv(p) Derivative of the power transform deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of the power transform link function in

statsmodels.discrete.discrete_model.Probit.pdf Probit.pdf(X) [source] Probit (Normal) probability density function Parameters: X : array-like The linear predictor of the model (XB). Returns: pdf : ndarray The value of the normal density function for each point of X. Notes This function is just an alias for scipy.stats.norm.pdf

statsmodels.tsa.arima_process.ArmaProcess.acf ArmaProcess.acf(nobs=None) [source] theoretical autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned acf Returns: acf : array autocorrelation of ARMA process given by ar, ma See also arma_aco