- class statsmodels.miscmodels.tmodel.TLinearModel(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds)¶
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.
expand to full parameter array when some parameters are fixed
fit([start_params, method, maxiter, ...])
Fit method for likelihood based models
from_formula(formula, data[, subset, drop_cols])
Create a Model from a formula and dataframe.
Hessian of log-likelihood evaluated at params
hessian_factor(params[, scale, observed])
Weights for calculating Hessian
Fisher information matrix of model.
Initialize (possibly re-initialize) a Model instance.
Log-likelihood of model at params
Log-likelihood of the model for all observations at params.
Negative log-likelihood of model at params
Loglikelihood of linear model with t distributed errors.
After a model has been fit predict returns the fitted values.
Gradient of log-likelihood evaluated at params
Jacobian/Gradient of log-likelihood evaluated at params for each observation.
Names of endogenous variables.
Names of exogenous variables.