How to compare interpretatively different models for the conditional variance. Juutilainen I, R., J. Journal of Applied Statistics, 37(5):983-998, 2010.
abstract   bibtex   
This study considers regression-type models with heteroscedastic Gaussian errors. The conditional variance is assumed to depend on the explanatory variables via a parametric or non-parametric variance function. The variance function has usually been selected on the basis of the log-likelihoods of fitted models. However, log-likelihood is a difficult quantity to interpret --- the practical importance of differences in log-likelihoods have been difficult to assess. This study overcomes these difficulties by transforming the difference in log-likelihood to easily interpretative difference in the error of predicted deviation. In addition, methods for testing the statistical significance of the observed difference in test data log-likelihood are proposed.
@article{
 title = {How to compare interpretatively different models for the conditional variance},
 type = {article},
 year = {2010},
 pages = {983-998},
 volume = {37},
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 last_modified = {2019-11-19T13:47:50.782Z},
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 abstract = {This study considers regression-type models with heteroscedastic
Gaussian errors. The conditional variance is assumed to depend on
the explanatory variables via a parametric or non-parametric
variance function. The variance function has usually been selected
on the basis of the log-likelihoods of fitted models. However,
log-likelihood is a difficult quantity to interpret --- the
practical importance of differences in log-likelihoods have been
difficult to assess. This study overcomes these difficulties by
transforming the difference in log-likelihood to easily
interpretative difference in the error of predicted deviation. In
addition, methods for testing the statistical significance of the
observed difference in test data log-likelihood are proposed.},
 bibtype = {article},
 author = {Juutilainen I, Röning J},
 journal = {Journal of Applied Statistics},
 number = {5}
}
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