Resumen de Two modeling strategies for empirical Bayes estimation

Bradley Efron

• 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.