statsmodels.sandbox.regression.gmm.GMM.fitgmm_cu GMM.fitgmm_cu(start, optim_method='bfgs', optim_args=None) [source] 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.robust.robust_linear_model.RLMResults.summary RLMResults.summary(yname=None, xname=None, title=0, alpha=0.05, return_fmt='text') [source] This is for testing the new summary setup

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid_centered_split static GEEResults.resid_centered_split() [source] Returns the residuals centered within each group. The residuals are returned as a list of arrays containing the centered residuals for each cluster.

statsmodels.nonparametric.bandwidths.bw_silverman statsmodels.nonparametric.bandwidths.bw_silverman(x, kernel=None) [source] Silverman?s Rule of Thumb Parameters: x : array-like Array for which to get the bandwidth kernel : CustomKernel object Unused Returns: bw : float The estimate of the bandwidth Notes Returns .9 * A * n ** (-1/5.) where A = min(std(x, ddof=1), IQR/1.349) IQR = np.subtract.reduce(np.percentile(x, [75,25])) References Silverman, B.W. (1986) Density Estimation.

statsmodels.regression.linear_model.GLS.loglike GLS.loglike(params) [source] Returns the value of the Gaussian log-likelihood function at params. Given the whitened design matrix, the log-likelihood is evaluated at the parameter vector params for the dependent variable endog. Parameters: params : array-like The parameter estimates Returns: loglike : float The value of the log-likelihood function for a GLS Model. Notes The log-likelihood function for the normal distribution is Y an

statsmodels.discrete.discrete_model.NegativeBinomial.pdf NegativeBinomial.pdf(X) The probability density (mass) function of the model.

statsmodels.discrete.discrete_model.NegativeBinomial.fit NegativeBinomial.fit(start_params=None, method='bfgs', maxiter=35, full_output=1, disp=1, callback=None, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) [source]

statsmodels.tools.eval_measures.bic statsmodels.tools.eval_measures.bic(llf, nobs, df_modelwc) [source] Bayesian information criterion (BIC) or Schwarz criterion Parameters: llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns: bic : float information criterion References http://en.wikipedia.org/wiki/Bayesian_information_criterion

statsmodels.sandbox.regression.try_catdata.groupstatsbin statsmodels.sandbox.regression.try_catdata.groupstatsbin(factors, values) [source] uses np.bincount, assumes factors/labels are integers

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.