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researcher

J. Berger

24 papers hereh-index 7534.7k citations278 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author8
  • middle author8
  • last author7

Across the 23 of 24 papers where every author was matched, so the position is known.

fields
  • math.ST16
  • stat.ME4
  • astro-ph1
  • astro-ph.IM1
  • stat.AP1
  • stat.CO1
same name
  • J. Berger — 132 papers, h 47
  • J. Berger — 63 papers
  • J. Berger — 50 papers, h 27
  • J. Berger — 32 papers, h 9
  • J. Berger — 31 papers, h 13
  • J. Berger — 29 papers, h 19

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20032018
most citedBayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

600 citations · 2.1k across the 23 of their papers we have counts for

collaborators
Showing 2017 · math.STShow all

4 papers · 2 filters

math.ST2017★ 1 cited

A note on recent criticisms to Birnbaum's theorem

Víctor Peña, James O. Berger

In this note, we provide critical commentary on two articles that cast doubt on the validity and implications of Birnbaum's theorem: Evans (2013) and Mayo (2014). In our view, the…

math.ST2017

Restricted type II maximum likelihood priors on regression coefficients

Víctor Peña, James O. Berger

In Bayesian hypothesis testing and model selection, prior distributions must be chosen carefully. For example, setting arbitrarily large prior scales for location parameters, which…

math.ST2017★ 1 cited

On the Ubiquity of Information Inconsistency for Conjugate Priors

Joris Mulder, James O. Berger, Víctor Peña +1

Informally, "Information Inconsistency" is the property that has been observed in many Bayesian hypothesis testing and model selection procedures whereby the Bayesian conclusion do…

math.ST2017★ 4 cited

Robust Gaussian Stochastic Process Emulation

Mengyang Gu, Xiaojing Wang, James O. Berger

We consider estimation of the parameters of a Gaussian Stochastic Process (GaSP), in the context of emulation (approximation) of computer models for which the outcomes are real-val…

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