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Bayesian Analysis of Errors-in-Variables Regression Models

Petros Dellaportas and David A. Stephens
Biometrics
Vol. 51, No. 3 (Sep., 1995), pp. 1085-1095
DOI: 10.2307/2533007
Stable URL: http://www.jstor.org/stable/2533007
Page Count: 11
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Bayesian Analysis of Errors-in-Variables Regression Models
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Abstract

Use of errors-in-variables models is appropriate in many practical experimental problems. However, inference based on such models is by no means straightforward. In previous analyses, simplifying assumptions have been made in order to ease this intractability, but assumptions of this nature are unfortunate and restrictive. In this paper, we analyse errors-in-variables models in full generality under a Bayesian formulation. In order to compute the necessary posterior distributions, we utilize various computational techniques. Two specific non-linear errors-in-variables regression examples are considered; the first is a re-analysed Berkson-type model, and the second is a classical errors-in-variables model. Our analyses are compared and contrasted with those presented else-where in the literature.

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