> Also, I think that the approach suggested by Jeff is problematic when there
> are strata. After constructing the model-averaged estimate and it's variance,
> a lot of work needs done to get the individual strata abundance estimates
> (and that's not straightforward for the novice Distance Sampling user). Also,
> won't the model-averaged estimate and it's variance be tricky when using
> different Key functions between candidate models? And how about with
> MCDS, where we may have different scale parameters? Maybe I'm missing
All you need to do is model average the density estimates which can be
done with either the total estimate or the individual stratum estimates
that DISTANCE produces. Unless you have small sample sizes like Rich or
poor data, much of this is a rather academic question because the
abundance estimates vary little between detection models which is why it
has never been much of a pressing issue. That could change in expanding
distance sampling to a full likelihood approach that models abundance
spatially as well as detection.
Falk, I didn't understand what you were getting at and you made some
fairly far strong statements without any apparent support for them.
> Works Referenced
> Buckland, S. T., K. P. Burnham, and N. H. Augustin. 1997. Model selection: an
> integral part of inference. Biometrics 53:603-618.
> Burnham, K. P., and D. R. Anderson. 2002. Model selection and multi-model
> inference: a practical information-theoretic approach. Second Edition edition.
> Springer, New York.
> Eric D. Stolen, Ph.D.
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