Finite Mixture Analysis of Beauty-Contest Data from Multiple Samples
This paper develops a finite mixture distribution analysis of Beauty-Contest data obtained from diverse groups of experiments. ML estimation using the EM approach provides estimates for the means and variances of the component distributions, which are common to all the groups, and estimates of the mixing proportions, which are specific to each group. This estimation is performed without imposing constraints on the parameters of the composing distributions. The statistical analysis indicates that many individuals follow a common pattern of reasoning described as iterated best reply (degenerate), and shows that the proportions of people thinking at different levels of depth vary across groups.
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- Peter Arcidiacono & John Bailey Jones, 2003.
"Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm,"
Econometric Society, vol. 71(3), pages 933-946, 05.
- Arcidiacono, Peter & Jones, John B., 2000. "Finite Mixture Distribution, Sequential Likelihood, and the EM Algorithm," Working Papers 00-16, Duke University, Department of Economics.
- Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
- Nagel, Rosemarie, 1995. "Unraveling in Guessing Games: An Experimental Study," American Economic Review, American Economic Association, vol. 85(5), pages 1313-26, December.
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