Linear Model with t distributed errors in the StatsModels library
In this recipe, we explain Linear Model with T-Distributed Errors (TLinearModel) in StatsModels.
Recipe Objective - What is Linear Model with t-distributed errors (TLinearModel) in the StatsModels library?
TLinearModel is a class that shows that you can define a new model simply by specifying the log-likelihood method. All result statistics are inherited from standard likelihood models and result classes. The results were tested against R in a simple case.
TLinearModel is a Maximum Likelihood Estimation of Linear Model with t-distributed errors. This is an example of generic MLE. All methods and results are standard, except for the definition of the negative log-likelihood method. Gradients and Hessian matrices, and all the resulting statistics, are based on numerical differentiation.
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It belongs to a class statsmodels.miscmodels.tmodel.TLinearModel(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds)
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Table of Contents
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 method for likelihood based models
from_formula(formula, data[, subset, drop_cols])
Create a Model from a formula and dataframe.
hessian(params)
Hessian of log-likelihood evaluated at params
information(params)
Fisher information matrix of model.
predict(params[, exog])
After a model has been fit predict returns the fitted values.
predict_distribution(exog)
return frozen scipy.stats distribution with mu at estimated prediction
score(params)
Gradient of log-likelihood evaluated at params
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