A note on the solution of the intrinsic equations

• Autores: Juan Antonio Cano Sánchez , Mathieu Kessler , Elías Moreno Bas
• Localización: XXVI Congreso Nacional de Estadística e Investigación Operativa: Úbeda, 6-9 de noviembre de 2001, 2001, ISBN 84-8439-080-2
• Idioma: inglés
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• Resumen

  • Defaults priors are topically used in Bayesian inference when there is no subjective information on the unknown parameters. In model selection and prediction the conventional default priors are not suitable because they are improper. However, they can be converted in appropiate priors via the solution of the functional intrinsic equations introduced in Berger and Pericchi (1996) and partially studied in Moreno, Bertolino and Racugno (1998). While the solution to these equations is known for nested models, to establishing conditions to characterize the solution for non-nested models is still an open problem. In this paper we push forward this problem and give illustrations of the obtained results