Two modeling strategies for empirical Bayes estimation

• Autores: Bradley Efron
• Localización: Statistical science, ISSN 0883-4237, Vol. 29, Nº. 2, 2014, págs. 285-301
• Idioma: inglés
• DOI: 10.1214/13-sts455
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• Resumen

  • Empirical Bayes methods use the data from parallel experiments, for instance, observations Xk ¡« N(Θk, 1) for k = 1, 2, . . . , N, to estimate the conditional distributions Θk|Xk. There are two main estimation strategies:

    modeling on the ¦È space, called ¡°g-modeling¡± here, and modeling on the x space, called ¡°f -modeling.¡± The two approaches are described and compared. A series of computational formulas are developed to assess their frequentist accuracy. Several examples, both contrived and genuine, show the strengths and limitations of the two strategies.