3.7.4. statsmodels.miscmodels.tmodel

Linear Model with Student-t distributed errors

Because the t distribution has fatter tails than the normal distribution, it can be used to model observations with heavier tails and observations that have some outliers. For the latter case, the t-distribution provides more robust estimators for mean or mean parameters (what about var?).

3.7.4.1. References

Kenneth L. Lange, Roderick J. A. Little, Jeremy M. G. Taylor (1989) Robust Statistical Modeling Using the t Distribution Journal of the American Statistical Association Vol. 84, No. 408 (Dec., 1989), pp. 881-896 Published by: American Statistical Association Stable URL: http://www.jstor.org/stable/2290063

not read yet

Created on 2010-09-24 Author: josef-pktd License: BSD

3.7.4.2. TODO

  • add starting values based on OLS

  • bugs: store_params doesn’t seem to be defined, I think this was a module

    global for debugging - commented out

  • parameter restriction: check whether version with some fixed parameters works

3.7.4.3. Classes

Arma(endog[, exog]) univariate Autoregressive Moving Average model, conditional on initial values
GenericLikelihoodModel(endog[, exog, ...]) Allows the fitting of any likelihood function via maximum likelihood.
TArma(endog[, exog]) Univariate Arma Model with t-distributed errors
TLinearModel(endog[, exog, loglike, score, ...]) Maximum Likelihood Estimation of Linear Model with t-distributed errors