statsmodels.genmod.cov_struct.Nested.summary Nested.summary() [source] Returns a summary string describing the state of the dependence structure.

statsmodels.iolib.table.SimpleTable.as_text SimpleTable.as_text(**fmt_dict) [source] Return string, the table as text.

statsmodels.miscmodels.tmodel.TLinearModel.hessian TLinearModel.hessian(params) Hessian of log-likelihood evaluated at params

statsmodels.regression.linear_model.GLS.fit_regularized GLS.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 : scalar or array-

statsmodels.nonparametric.kernel_regression.KernelReg class statsmodels.nonparametric.kernel_regression.KernelReg(endog, exog, var_type, reg_type='ll', bw='cv_ls', defaults=) [source] Nonparametric kernel regression class. Calculates the conditional mean E[y|X] where y = g(X) + e. Note that the ?local constant? type of regression provided here is also known as Nadaraya-Watson kernel regression; ?local linear? is an extension of that which suffers less from bias issues at the edge of the supp

Dates in timeseries models Link to Notebook GitHub In [1]: from __future__ import print_function import statsmodels.api as sm import numpy as np import pandas as pd Getting started In [2]: data = sm.datasets.sunspots.load() Right now an annual date series must be datetimes at the end of the year. In [3]: from datetime import datetime dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog)) Using Pandas Make a pandas TimeSeries

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.sandbox.regression.gmm.IVGMMResults.wald_test IVGMMResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tup

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