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)

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 via 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 the model, setting up parameter names and start values
loglike(params)	Loglikelihood of the model evaluated at params
loglikeobs(params)	Log-likelihood of the model for all observations at params
nloglike(params)	Negative log-likelihood of model at params
nloglikeobs(params)	Negative loglikelihood of linear model with t distributed errors
predict(params[, exog])	Return predicted mean 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 via 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 the model, setting up parameter names and start values
loglike(params)	Loglikelihood of the model evaluated at params
loglikeobs(params)	Log-likelihood of the model for all observations at params
nloglike(params)	Negative log-likelihood of model at params
nloglikeobs(params)	Negative loglikelihood of linear model with t distributed errors
predict(params[, exog])	Return predicted mean 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