statsmodels.genmod.families.links.identity.deriv2 identity.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.regression.linear_model.OLSResults.cov_params OLSResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like, opti

Vector Autoregressions tsa.vector_ar VAR(p) processes We are interested in modeling a multivariate time series , where denotes the number of observations and the number of variables. One way of estimating relationships between the time series and their lagged values is the vector autoregression process: where is a coefficient matrix. We follow in large part the methods and notation of Lutkepohl (2005), which we will not develop here. Model fitting Note The classes referenced below ar

statsmodels.sandbox.distributions.transformed.ExpTransf_gen class statsmodels.sandbox.distributions.transformed.ExpTransf_gen(kls, *args, **kwargs) [source] Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_

statsmodels.regression.linear_model.GLS.information GLS.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.regression.gmm.IVRegressionResults.eigenvals static IVRegressionResults.eigenvals() Return eigenvalues sorted in decreasing order.

statsmodels.sandbox.regression.gmm.LinearIVGMM.fitstart LinearIVGMM.fitstart()

statsmodels.discrete.discrete_model.DiscreteResults.initialize DiscreteResults.initialize(model, params, **kwd)

statsmodels.discrete.discrete_model.BinaryModel.score BinaryModel.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.est_loc_scale LogTransf_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.