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 differentiation.
Attributes
endog_names
Names of endogenous variables. exog_names
Names of exogenous variables. Methods
expandparams
(params)expand to full parameter array when some parameters are fixed fit
([start_params, method, maxiter, …])Fit the model using maximum likelihood. from_formula
(formula, data[, subset, drop_cols])Create a Model from a formula and dataframe. hessian
(params)Hessian of log-likelihood evaluated at params hessian_factor
(params[, scale, observed])Weights for calculating Hessian information
(params)Fisher information matrix of model. initialize
()Initialize (possibly re-initialize) a Model instance. loglike
(params)Log-likelihood of model at params loglikeobs
(params)Log-likelihood of individual observations at params nloglike
(params)Negative log-likelihood of model at params nloglikeobs
(params)Loglikelihood of linear model with t distributed errors. predict
(params[, exog])After a model has been fit predict returns the fitted values. reduceparams
(params)Reduce parameters score
(params)Gradient of log-likelihood evaluated at params score_obs
(params, **kwds)Jacobian/Gradient of log-likelihood evaluated at params for each observation. Methods
expandparams
(params)expand to full parameter array when some parameters are fixed fit
([start_params, method, maxiter, …])Fit the model using maximum likelihood. from_formula
(formula, data[, subset, drop_cols])Create a Model from a formula and dataframe. hessian
(params)Hessian of log-likelihood evaluated at params hessian_factor
(params[, scale, observed])Weights for calculating Hessian information
(params)Fisher information matrix of model. initialize
()Initialize (possibly re-initialize) a Model instance. loglike
(params)Log-likelihood of model at params loglikeobs
(params)Log-likelihood of individual observations at params nloglike
(params)Negative log-likelihood of model at params nloglikeobs
(params)Loglikelihood of linear model with t distributed errors. predict
(params[, exog])After a model has been fit predict returns the fitted values. reduceparams
(params)Reduce parameters score
(params)Gradient of log-likelihood evaluated at params score_obs
(params, **kwds)Jacobian/Gradient of log-likelihood evaluated at params for each observation. Properties
endog_names
Names of endogenous variables. exog_names
Names of exogenous variables.