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.
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])Create a Model from a formula and dataframe. hessian
(params)Hessian of log-likelihood evaluated at params information
(params)Fisher information matrix of model initialize
()jac
(*args, **kwds)jac is deprecated, use score_obs instead! loglike
(params)loglikeobs
(params)nloglike
(params)nloglikeobs
(params)Loglikelihood of linear model with t distributed errors. predict
(params[, exog])reduceparams
(params)score
(params)Gradient of log-likelihood evaluated at params score_obs
(params, **kwds)Jacobian/Gradient of log-likelihood evaluated at params for each observation. Attributes
endog_names
exog_names