statsmodels.stats.weightstats.DescrStatsW.demeaned static DescrStatsW.demeaned() [source] data with weighted mean subtracted

statsmodels.robust.norms.LeastSquares.psi LeastSquares.psi(z) [source] The psi function for the least squares estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z

statsmodels.regression.mixed_linear_model.MixedLM.fit MixedLM.fit(start_params=None, reml=True, niter_em=0, niter_sa=0, do_cg=True, fe_pen=None, cov_pen=None, free=None, full_output=False, **kwargs) [source] Fit a linear mixed model to the data. Parameters: start_params: array-like or MixedLMParams : If a MixedLMParams the state provides the starting value. If array-like, this is the packed parameter vector, assumed to be in the same state as this model. reml : bool If true, fit accordin

statsmodels.genmod.cov_struct.Exchangeable.initialize Exchangeable.initialize(model) Called by GEE, used by implementations that need additional setup prior to running fit. Parameters: model : GEE class A reference to the parent GEE class instance.

statsmodels.genmod.families.links.cauchy.inverse_deriv cauchy.inverse_deriv(z) Derivative of the inverse of the CDF transformation link function Parameters: z : array The inverse of the link function at p Returns: The value of the derivative of the inverse of the logit function :

statsmodels.tsa.varma_process.VarmaPoly.getisstationary VarmaPoly.getisstationary(a=None) [source] check whether the auto-regressive lag-polynomial is stationary Returns: isstationary : boolean *attaches* : areigenvalues : complex array eigenvalues sorted by absolute value References formula taken from NAG manual

statsmodels.nonparametric.kernel_density.KDEMultivariate.pdf KDEMultivariate.pdf(data_predict=None) [source] Evaluate the probability density function. Parameters: data_predict: array_like, optional : Points to evaluate at. If unspecified, the training data is used. Returns: pdf_est: array_like : Probability density function evaluated at data_predict. Notes The probability density is given by the generalized product kernel estimator:

statsmodels.sandbox.regression.gmm.IVGMM.fitgmm_cu IVGMM.fitgmm_cu(start, optim_method='bfgs', optim_args=None) estimate parameters using continuously updating GMM Parameters: start : array_like starting values for minimization Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin

statsmodels.sandbox.distributions.extras.ACSkewT_gen.freeze ACSkewT_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.fit_loc_scale TransfTwo_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.