statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.T classmethod KalmanFilter.T(params, r, k, p) [source] The coefficient matrix for the state vector in the state equation. Its dimension is r+k x r+k. Parameters: r : int In the context of the ARMA model r is max(p,q+1) where p is the AR order and q is the MA order. k : int The number of exogenous variables in the ARMA model, including the constant if appropriate. p : int The AR coefficient in an ARMA model. References Durbin and Koopm

statsmodels.regression.mixed_linear_model.MixedLMResults.profile_re MixedLMResults.profile_re(re_ix, num_low=5, dist_low=1.0, num_high=5, dist_high=1.0) [source] Calculate a series of values along a 1-dimensional profile likelihood. Parameters: re_ix : integer The index of the variance parameter for which to construct a profile likelihood. num_low : integer The number of points at which to calculate the likelihood below the MLE of the parameter of interest. dist_low : float The distanc

statsmodels.regression.mixed_linear_model.MixedLM.steepest_ascent MixedLM.steepest_ascent(params, n_iter) [source] Take steepest ascent steps to increase the log-likelihood function. Parameters: params : array-like The starting point of the optimization. n_iter: non-negative integer : Return once this number of iterations have occured. Returns: A MixedLMParameters object containing the final value of the : optimization. :

statsmodels.sandbox.distributions.transformed.Transf_gen.logcdf Transf_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution function at x of the given RV. 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 scale parameter (default=1) Returns: logcdf : array_like

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.entropy TransfTwo_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: 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 Scale parameter (default=1).

statsmodels.sandbox.distributions.transformed.LogTransf_gen.mean LogTransf_gen.mean(*args, **kwds) Mean of the distribution Parameters: 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 scale parameter (default=1) Returns: mean : float the mean of the distribution

statsmodels.genmod.generalized_linear_model.GLMResults.summary GLMResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns: smr

statsmodels.tools.numdiff.approx_hess2 statsmodels.tools.numdiff.approx_hess2(x, f, epsilon=None, args=(), kwargs={}, return_grad=False) [source] Calculate Hessian with finite difference derivative approximation Parameters: x : array_like value at which function derivative is evaluated f : function function of one array f(x, *args, **kwargs) epsilon : float or array-like, optional Stepsize used, if None, then stepsize is automatically chosen according to EPS**(1/3)*x. args : tuple Ar

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.miscmodels.tmodel.TLinearModel.nloglikeobs TLinearModel.nloglikeobs(params) [source] Loglikelihood of linear model with t distributed errors. Parameters: params : array The parameters of the model. The last 2 parameters are degrees of freedom and scale. Returns: loglike : array, (nobs,) The log likelihood of the model evaluated at params for each observation defined by self.endog and self.exog. Notes The t distribution is the standard t distribution and not a standardi