statsmodels.miscmodels.count.PoissonZiGMLE class statsmodels.miscmodels.count.PoissonZiGMLE(endog, exog=None, offset=None, missing='none', **kwds) [source] Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset and zero-inflation. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numer

statsmodels.miscmodels.count.PoissonGMLE class statsmodels.miscmodels.count.PoissonGMLE(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds) [source] Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are b

Maximum Likelihood Estimation (Generic models) Link to Notebook GitHub This tutorial explains how to quickly implement new maximum likelihood models in statsmodels. We give two examples: Probit model for binary dependent variables Negative binomial model for count data The GenericLikelihoodModel class eases the process by providing tools such as automatic numeric differentiation and a unified interface to scipy optimization functions. Using statsmodels, users can fit new MLE models simply

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.distributions.transformed.LogTransf_gen.var LogTransf_gen.var(*args, **kwds) Variance of the distribution 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) Returns: var : float the variance of the distribution

statsmodels.sandbox.distributions.transformed.LogTransf_gen.stats LogTransf_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.transformed.LogTransf_gen.std LogTransf_gen.std(*args, **kwds) Standard deviation of the distribution. 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) Returns: std : float standard deviation of the distribution

statsmodels.sandbox.distributions.transformed.LogTransf_gen.sf LogTransf_gen.sf(x, *args, **kwds) Survival function (1-cdf) at x of the given RV. Parameters: x : array_like quantiles 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) Returns: sf : array_like Survival function evalu

statsmodels.sandbox.distributions.transformed.LogTransf_gen.rvs LogTransf_gen.rvs(*args, **kwds) Random variates of given type. 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). size : int or tuple of ints, optional Defining number of random variates (default=1). Retu

statsmodels.sandbox.distributions.transformed.LogTransf_gen.ppf LogTransf_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability 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) Returns: x : array_l