statsmodels.sandbox.distributions.extras.SkewNorm2_gen.moment SkewNorm2_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.miscmodels.tmodel.TLinearModel class statsmodels.miscmodels.tmodel.TLinearModel(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds) [source] Maximum Likelihood Estimation of Linear Model with t-distributed errors This is an example for generic MLE. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiati

statsmodels.sandbox.tsa.fftarma.ArmaFft.padarr ArmaFft.padarr(arr, maxlag, atend=True) [source] pad 1d array with zeros at end to have length maxlag function that is a method, no self used Parameters: arr : array_like, 1d array that will be padded with zeros maxlag : int length of array after padding atend : boolean If True (default), then the zeros are added to the end, otherwise to the front of the array Returns: arrp : ndarray zero-padded array Notes This is mainly written to

statsmodels.robust.norms.AndrewWave.rho AndrewWave.rho(z) [source] The robust criterion function for Andrew?s wave. Parameters: z : array-like 1d array Returns: rho : array rho(z) = a*(1-cos(z/a)) for |z| <= a*pi rho(z) = 2*a for |z| > a*pi

statsmodels.sandbox.stats.runs.Runs.runs_test Runs.runs_test(correction=True) [source] basic version of runs test Parameters: correction: bool : Following the SAS manual, for samplesize below 50, the test statistic is corrected by 0.5. This can be turned off with correction=False, and was included to match R, tseries, which does not use any correction. pvalue based on normal distribution, with integer correction :

statsmodels.nonparametric.kde.KDEUnivariate.fit KDEUnivariate.fit(kernel='gau', bw='normal_reference', fft=True, weights=None, gridsize=None, adjust=1, cut=3, clip=(-inf, inf)) [source] Attach the density estimate to the KDEUnivariate class. Parameters: kernel : str The Kernel to be used. Choices are: ?biw? for biweight ?cos? for cosine ?epa? for Epanechnikov ?gau? for Gaussian. ?tri? for triangular ?triw? for triweight ?uni? for uniform bw : str, float The bandwidth to use. Choices are:

statsmodels.sandbox.stats.multicomp.varcorrection_unequal statsmodels.sandbox.stats.multicomp.varcorrection_unequal(var_all, nobs_all, df_all) [source] return joint variance from samples with unequal variances and unequal sample sizes something is wrong Parameters: var_all : array_like The variance for each sample nobs_all : array_like The number of observations for each sample df_all : array_like degrees of freedom for each sample Returns: varjoint : float joint variance. dfjoint

statsmodels.genmod.families.links.inverse_squared.inverse_deriv inverse_squared.inverse_deriv(z) Derivative of the inverse of the power transform Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the power transform : function :

statsmodels.duration.hazard_regression.PHReg.robust_covariance PHReg.robust_covariance(params) [source] Returns a covariance matrix for the proportional hazards model regresion coefficient estimates that is robust to certain forms of model misspecification. Parameters: params : ndarray The parameter vector at which the covariance matrix is calculated. Returns: The robust covariance matrix as a square ndarray. : Notes This function uses the groups argument to determine groups within whi

statsmodels.genmod.generalized_estimating_equations.GEEResults.predict GEEResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pa