statsmodels.miscmodels.tmodel.TLinearModel.fit TLinearModel.fit(start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit

statsmodels.nonparametric.kernel_regression.KernelCensoredReg class statsmodels.nonparametric.kernel_regression.KernelCensoredReg(endog, exog, var_type, reg_type, bw='cv_ls', censor_val=0, defaults=) [source] Nonparametric censored regression. Calculates the condtional mean E[y|X] where y = g(X) + e, where y is left-censored. Left censored variable Y is defined as Y = min {Y', L} where L is the value at which Y is censored and Y' is the true value of the variable. Parameters: endog: list wi

statsmodels.sandbox.distributions.transformed.LogTransf_gen.entropy LogTransf_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional Location parameter (default=0). scale : array_like, optional Scale parameter (default=1).

statsmodels.sandbox.distributions.extras.NormExpan_gen.nnlf NormExpan_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.genmod.generalized_estimating_equations.GEEMargins.pvalues static GEEMargins.pvalues() [source]

statsmodels.genmod.families.links.identity.inverse identity.inverse(z) Inverse of the power transform link function Parameters: `z` : array-like Value of the transformed mean parameters at p Returns: `p` : array Mean parameters Notes g^(-1)(z`) = z`**(1/`power)

statsmodels.discrete.discrete_model.DiscreteModel.fit_regularized DiscreteModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=True, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, qc_verbose=False, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, o

statsmodels.genmod.generalized_linear_model.GLMResults.initialize GLMResults.initialize(model, params, **kwd)

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.stats ExpTransf_gen.stats(*args, **kwds) Some statistics of the given RV Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which mo

statsmodels.sandbox.distributions.extras.pdf_moments statsmodels.sandbox.distributions.extras.pdf_moments(cnt) [source] Return the Gaussian expanded pdf function given the list of central moments (first one is mean). Changed so it works only if four arguments are given. Uses explicit formula, not loop. Notes This implements a Gram-Charlier expansion of the normal distribution where the first 2 moments coincide with those of the normal distribution but skew and kurtosis can deviate from it. I