Abstract
Thurstonian models provide a flexible framework for the analysis of multiple paired comparison judgments because they allow a wide range of hypotheses about the judgments' mean and covariance structures to be tested. However, applications have been limited to a large extent by the computational intractability involved in fitting this class of models. This paper demonstrates that the Monte Carlo EM algorithm facilitates maximum likelihood estimation of Thurstonian paired comparison models even when the number of items is large. A paired comparison study is presented in detail to illustrate the estimation approach.
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