[R-sig-ME] How to assess significance of variance components (Please discard previous e mail... but read this one)
chantepie at mnhn.fr
chantepie at mnhn.fr
Thu Oct 18 11:52:19 CEST 2012
My question could appear trivial but I still have not found a clear answer.
From what I gathered, the Bayesian framework gives us two possible
tools to estimate significance of variances, the DIC and Confidence
Interval (CI) estimates
DIC allows to compare models and test for significance of variances.
Some papers mention that this approach is valid for all the
exponential family distribution models, to the extent that it should
even allow to test which distribution fits better the data. However,
in a previous post Jarrod mentioned that DIC does not always answer
the hypothesis we want to test and finished by saying that for non
gaussian distribution, he?d never use DIC. And actually, when running
some animal models with Poisson distribution I encountered strange
results suggesting that DIC does not work at all. The lower CI of Va
estimates are clearly greater than 0 which let me think that Va is
different from 0 but DIC does not give substantial support for models
I understand that assessing confidence interval is the great advantage
of Bayesian models. But as variances range between [0:1], it is not
possible to construct a Pvalue by counting the proportion of estimate
below 0 (as we could do with covariances). When the lower CI of a
variance is far from 0, it is quite easy to be sure that this variance
is different from 0 but when the posterior modes are small and lower
CI are close to 0, how can we decide? One approach could be to check
whether the posterior mode is well defined or whether it ?collapses?
on zero but would that be enough?
I am aware that Bayesian statistics are not frequentist statistics and
statistical tools are different but, a clear decision rule would be
It would be most helpful to know your thoughts about this and whether
there are other decision rules that could be applied.
Thanks to all
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