3.7.4.3.4. statsmodels.miscmodels.tmodel.TLinearModel¶
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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.
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__init__
(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds)¶
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3.7.4.3.4.1. Methods¶
__init__ (endog[, exog, loglike, score, ...]) |
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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]) |
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 () |
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jac (*args, **kwds) |
jac is deprecated, use score_obs instead! |
loglike (params) |
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loglikeobs (params) |
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nloglike (params) |
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nloglikeobs (params) |
Loglikelihood of linear model with t distributed errors. |
predict (params[, exog]) |
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reduceparams (params) |
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score (params) |
Gradient of log-likelihood evaluated at params |
score_obs (params, **kwds) |
Jacobian/Gradient of log-likelihood evaluated at params for each observation. |
3.7.4.3.4.2. Attributes¶
endog_names |
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exog_names |