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

hessian_factor(params[, scale, observed])

Weights for calculating Hessian

information(params)

Fisher information matrix of model

initialize()

Initialize (possibly re-initialize) a Model instance.

loglike(params)

Log-likelihood of model at params

loglikeobs(params)

Log-likelihood of individual observations at params

nloglike(params)

Negative log-likelihood of model at params

nloglikeobs(params)

Loglikelihood of linear model with t distributed errors.

predict(params[, exog])

After a model has been fit predict returns the fitted 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.