statsmodels.regression.quantile_regression.QuantReg.fit_regularized QuantReg.fit_regularized(method='coord_descent', maxiter=1000, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-08, zero_tol=1e-08, **kwargs) Return a regularized fit to a linear regression model. Parameters: method : string Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha :

statsmodels.regression.linear_model.GLSAR class statsmodels.regression.linear_model.GLSAR(endog, exog=None, rho=1, missing='none', **kwargs) [source] A regression model with an AR(p) covariance structure. Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodel

statsmodels.miscmodels.count.PoissonGMLE.hessian PoissonGMLE.hessian(params) Hessian of log-likelihood evaluated at params

statsmodels.graphics.functional.fboxplot statsmodels.graphics.functional.fboxplot(data, xdata=None, labels=None, depth=None, method='MBD', wfactor=1.5, ax=None, plot_opts={}) [source] Plot functional boxplot. A functional boxplot is the analog of a boxplot for functional data. Functional data is any type of data that varies over a continuum, i.e. curves, probabillity distributions, seasonal data, etc. The data is first ordered, the order statistic used here is banddepth. Plotted are then the

Linear Regression Linear models with independently and identically distributed errors, and for errors with heteroscedasticity or autocorrelation. This module allows estimation by ordinary least squares (OLS), weighted least squares (WLS), generalized least squares (GLS), and feasible generalized least squares with autocorrelated AR(p) errors. See Module Reference for commands and arguments. Examples # Load modules and data import numpy as np import statsmodels.api as sm spector_data = sm.datas

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.start_weights NonlinearIVGMM.start_weights(inv=True)

statsmodels.tsa.varma_process.VarmaPoly.hstackarma_minus1 VarmaPoly.hstackarma_minus1() [source] stack ar and lagpolynomial vertically in 2d array this is the Kalman Filter representation, I think

statsmodels.discrete.discrete_model.BinaryResults class statsmodels.discrete.discrete_model.BinaryResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for binary data Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterio

statsmodels.tsa.arima_model.ARIMAResults.hqic static ARIMAResults.hqic()

statsmodels.miscmodels.count.PoissonZiGMLE.initialize PoissonZiGMLE.initialize()