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scikits.statsmodels.miscmodels.tmodel.TLinearModel

class scikits.statsmodels.miscmodels.tmodel.TLinearModel(endog, exog=None, loglike=None, score=None, hessian=None)[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.

Methods

bsejac()
bsejhj()
covjac() covariance of parameters based on loglike outer product of jacobian
covjhj()
expandparams(params) expand to full parameter array when some parameters are fixed
fit([start_params, method, maxiter, ...]) Fit the model using maximum likelihood.
hessian(params) Hessian of log-likelihood evaluated at params
hessv()
information(params) Fisher information matrix of model
initialize()
jac(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each
jacv()
loglike(params)
loglikeobs(params)
nloglike(params)
nloglikeobs(params) Loglikelihood of linear model with t distributed errors.
predict(exog[, params]) After a model has been fit predict returns the fitted values.
reduceparams(params)
score(params) Gradient of log-likelihood evaluated at params

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