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.

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 using 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
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.

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, 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
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 Names of endogenous variables
exog_names Names of exogenous variables