7.14.2.1. 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.

__init__(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds)¶

Methods

__init__(endog[, exog, loglike, score, ...])
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