Re: [R] Do Users of Nonlinear Mixed Effects Models Know Whether Their Software Really Works?

From: Andrew Robinson <A.Robinson_at_ms.unimelb.edu.au>
Date: Fri 14 Oct 2005 - 08:28:43 EST

Dave,

that's an interesting start for a comparison. Let me point out some ways that you might construct a compelling argument. Of course, these aren't exhaustive, and others may well provide further depth.

  1. If I understand correctly, you're trying to estimate parameters from a real dataset. Why not try a simulated dataset, where you know exactly what the true values (and parameter distributions) are?
  2. Furthermore, an argument from one dataset isn't very convincing. The sample size for inference is too small. Why not repeat this procedure many times, sampling from the same base model?
  3. Then, you could also vary the structure of the underlying model systematically, and assess the comparison of fits as a function of the underlying model/dataset nexus.
  4. Next, a problem with the example (as I understand it) is that although you've computed what you call exact MLE's, I think that they're exact when conditioned on the model. Are they very robust to model misspecification? (I mean beyond large-sample theory).
  5. Finally, of course, then making the scripts available for forsenic investigations.

Cheers,

Andrew

On Thu, Oct 13, 2005 at 01:19:24PM -0700, dave fournier wrote:
> Do Users of Nonlinear Mixed Effects Models Know
>
> Whether Their Software Really Works?
>

-- 
Andrew Robinson
Senior Lecturer in Statistics                       Tel: +61-3-8344-9763
Department of Mathematics and Statistics            Fax: +61-3-8344-4599
University of Melbourne, VIC 3010 Australia
Email: a.robinson_at_ms.unimelb.edu.au    Website: http://www.ms.unimelb.edu.au

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Received on Fri Oct 14 08:41:37 2005

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