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.
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. |
loglikeobs (params) | |
nloglike (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) | |
score (params) | Gradient of log-likelihood evaluated at params |
score_obs (params, **kwds) | Jacobian/Gradient of log-likelihood evaluated at params for each observation. |
endog_names | Names of endogenous variables |
exog_names | Names of exogenous variables |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.miscmodels.tmodel.TLinearModel.html