statsmodels.tsa.arima_model.ARIMA class statsmodels.tsa.arima_model.ARIMA(endog, order, exog=None, dates=None, freq=None, missing='none') [source] Autoregressive Integrated Moving Average ARIMA(p,d,q) Model Parameters: endog : array-like The endogenous variable. order : iterable The (p,d,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should not include a constant or

statsmodels.stats.moment_helpers.mvsk2mc statsmodels.stats.moment_helpers.mvsk2mc(args) [source] convert mean, variance, skew, kurtosis to central moments

Generalized Linear Models (Formula) Link to Notebook GitHub This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models. To begin, we load the Star98 dataset and we construct a formula and pre-process the data: In [1]: from __future__ import print_function import statsmodels.api as sm import statsmodels.formula.api as smf star98 = sm.datasets.star98.load_pandas().data formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \

statsmodels.genmod.families.links.CDFLink.inverse CDFLink.inverse(z) [source] The inverse of the CDF link Parameters: z : array-like The value of the inverse of the link function at p Returns: p : array Mean probabilities. The value of the inverse of CDF link of z Notes g^(-1)(z) = dbn.cdf(z)

statsmodels.robust.robust_linear_model.RLM.score RLM.score(params) [source]

statsmodels.robust.robust_linear_model.RLMResults.initialize RLMResults.initialize(model, params, **kwd)

M-Estimators for Robust Linear Modeling Link to Notebook GitHub In [1]: from __future__ import print_function from statsmodels.compat import lmap import numpy as np from scipy import stats import matplotlib.pyplot as plt import statsmodels.api as sm An M-estimator minimizes the function $$Q(e_i, \rho) = \sum_i~\rho \left (\frac{e_i}{s}\right )$$ where $\rho$ is a symmetric function of the residuals The effect of $\rho$ is to reduce the influence of outliers $s$ is an estimate

statsmodels.sandbox.regression.gmm.GMMResults.t_test GMMResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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 tuple of array

statsmodels.regression.linear_model.RegressionResults.load classmethod RegressionResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.sandbox.distributions.extras.NormExpan_gen class statsmodels.sandbox.distributions.extras.NormExpan_gen(args, **kwds) [source] Gram-Charlier Expansion of Normal distribution class follows scipy.stats.distributions pattern but with __init__ Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...])