@article{
 title = {How to compare interpretatively different models for the conditional variance},
 type = {article},
 year = {2010},
 pages = {983-998},
 volume = {37},
 id = {8261a861-3726-3a2b-9a63-ee6f11328c26},
 created = {2019-11-19T13:01:04.353Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
 last_modified = {2019-11-19T13:47:50.782Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {isg:1332},
 source_type = {article},
 private_publication = {false},
 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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