Statistical Science

Rejoinder: Bayes, Oracle Bayes, and Empirical Bayes

Bradley Efron

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Article information

Source
Statist. Sci., Volume 34, Number 2 (2019), 234-235.

Dates
First available in Project Euclid: 19 July 2019

Permanent link to this document
https://projecteuclid.org/euclid.ss/1563501639

Digital Object Identifier
doi:10.1214/19-STS674REJ

Mathematical Reviews number (MathSciNet)
MR3983326

Citation

Efron, Bradley. Rejoinder: Bayes, Oracle Bayes, and Empirical Bayes. Statist. Sci. 34 (2019), no. 2, 234--235. doi:10.1214/19-STS674REJ. https://projecteuclid.org/euclid.ss/1563501639


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References

  • [1] Efron, B. (2011). Tweedie’s formula and selection bias. J. Amer. Statist. Assoc. 106 1602–1614.
  • [2] Efron, B. (2016). Empirical Bayes deconvolution estimates. Biometrika 103 1–20.
  • [3] Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Institute of Mathematical Statistics (IMS) Monographs 5. Cambridge Univ. Press, New York.
  • [4] Efron, B. and Narasimhan, B. (2019). A $g$-modeling program for deconvolution and empirical Bayes estimation. J. Stat. Softw. To appear.
  • [5] Good, I. J. and Toulmin, G. H. (1956). The number of new species, and the increase in population coverage, when a sample is increased. Biometrika 43 45–63.
  • [6] Jiang, W. and Zhang, C.-H. (2009). General maximum likelihood empirical Bayes estimation of normal means. Ann. Statist. 37 1647–1684.
  • [7] Koenker, R. and Mizera, I. (2014). Convex optimization, shape constraints, compound decisions, and empirical Bayes rules. J. Amer. Statist. Assoc. 109 674–685.
  • [8] Laird, N. (1978). Nonparametric maximum likelihood estimation of a mixed distribution. J. Amer. Statist. Assoc. 73 805–811.

See also