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Fitting Latent Variable Models for Paired Comparisons and Ranking Studies  An Application of Optimal Design Theory
http://sms.cam.ac.uk/media/1169592
Torsney, B (University of Glasgow)
Thursday 01 September 2011, 10:0010:30
40
Fitting Latent Variable Models for Paired Comparisons and Ranking Studies  An Application of Optimal Design Theory
http://sms.cam.ac.uk/media/1169592
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Fitting Latent Variable Models for Paired Comparisons and Ranking Studies  An Application of Optimal Design Theory
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http://sms.cam.ac.uk/media/1169592
Fitting Latent Variable Models for Paired Comparisons and Ranking Studies  An Application of Optimal Design Theory
Torsney, B (University of Glasgow)
Thursday 01 September 2011, 10:0010:30
Mon, 05 Sep 2011 10:47:18 +0100
Torsney, B
Steve Greenham
Isaac Newton Institute
Torsney, B
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Torsney, B (University of Glasgow)
Thursday 01 September 2011, 10:0010:30
Torsney, B (University of Glasgow)
Thursday 01 September 2011, 10:0010:30
Cambridge University
1723
http://sms.cam.ac.uk/media/1169592
Fitting Latent Variable Models for Paired Comparisons and Ranking Studies  An Application of Optimal Design Theory
Torsney, B (University of Glasgow)
Thursday 01 September 2011, 10:0010:30
In a paired comparisons experiment a subject has to indicate which of two 'treatments' Ti, Tj is preferred. We observe Oij, the frequency with which Ti is preferred to Tj.in nij comparisons. Under a class of models for such data, which include the Bradley Terry and Thurstone models, P(Ti is preferred to Tj) = F( i  j), where F(.) is a symmetric distribution function and ( i) is a treatment index. For identifiability purposes constraints must be imposed on parameters. One is to assume that ipi = 1, where pi = ln( i); an alternative is ipi = 1. Thus theorems identifying optimal design weights and algorithms for determining them carry over to the maximum likelihood estimation of these parameters.
Of course these tools can also be used to determine locally optimal designs for such models.
We will explore this fusion of topics, taking the opportunity to expand on the class of models, both for simple paired comparisons data and also for data consisting of orderings or rankings. In particular we will exploit multiplicative algorithms for maximum likelihood estimation.
20110905T10:47:27+01:00
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