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James O. Berger

Bio: James O. Berger is an academic researcher from Duke University. The author has contributed to research in topics: Prior probability & Bayesian probability. The author has an hindex of 71, co-authored 241 publications receiving 36488 citations. Previous affiliations of James O. Berger include University of Valencia & University College London.


Papers
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Book
22 Dec 2012
TL;DR: An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory.
Abstract: 1. Basic concepts 2. Utility and loss 3. Prior information and subjective probability 4. Bayesian analysis 5. Minimax analysis 6. Invariance 7. Preposterior and sequential analysis 8. Complete and essentially complete classes Appendices.

5,573 citations

Journal ArticleDOI
Daniel J. Benjamin1, James O. Berger2, Magnus Johannesson3, Magnus Johannesson1, Brian A. Nosek4, Brian A. Nosek5, Eric-Jan Wagenmakers6, Richard A. Berk7, Kenneth A. Bollen8, Björn Brembs9, Lawrence D. Brown7, Colin F. Camerer10, David Cesarini11, David Cesarini12, Christopher D. Chambers13, Merlise A. Clyde2, Thomas D. Cook14, Thomas D. Cook15, Paul De Boeck16, Zoltan Dienes17, Anna Dreber3, Kenny Easwaran18, Charles Efferson19, Ernst Fehr20, Fiona Fidler21, Andy P. Field17, Malcolm R. Forster22, Edward I. George7, Richard Gonzalez23, Steven N. Goodman24, Edwin J. Green25, Donald P. Green26, Anthony G. Greenwald27, Jarrod D. Hadfield28, Larry V. Hedges15, Leonhard Held20, Teck-Hua Ho29, Herbert Hoijtink30, Daniel J. Hruschka31, Kosuke Imai32, Guido W. Imbens24, John P. A. Ioannidis24, Minjeong Jeon33, James Holland Jones34, Michael Kirchler35, David Laibson36, John A. List37, Roderick J. A. Little23, Arthur Lupia23, Edouard Machery38, Scott E. Maxwell39, Michael A. McCarthy21, Don A. Moore40, Stephen L. Morgan41, Marcus R. Munafò42, Shinichi Nakagawa43, Brendan Nyhan44, Timothy H. Parker45, Luis R. Pericchi46, Marco Perugini47, Jeffrey N. Rouder48, Judith Rousseau49, Victoria Savalei50, Felix D. Schönbrodt51, Thomas Sellke52, Betsy Sinclair53, Dustin Tingley36, Trisha Van Zandt16, Simine Vazire54, Duncan J. Watts55, Christopher Winship36, Robert L. Wolpert2, Yu Xie32, Cristobal Young24, Jonathan Zinman44, Valen E. Johnson1, Valen E. Johnson18 
University of Southern California1, Duke University2, Stockholm School of Economics3, Center for Open Science4, University of Virginia5, University of Amsterdam6, University of Pennsylvania7, University of North Carolina at Chapel Hill8, University of Regensburg9, California Institute of Technology10, Research Institute of Industrial Economics11, New York University12, Cardiff University13, Mathematica Policy Research14, Northwestern University15, Ohio State University16, University of Sussex17, Texas A&M University18, Royal Holloway, University of London19, University of Zurich20, University of Melbourne21, University of Wisconsin-Madison22, University of Michigan23, Stanford University24, Rutgers University25, Columbia University26, University of Washington27, University of Edinburgh28, National University of Singapore29, Utrecht University30, Arizona State University31, Princeton University32, University of California, Los Angeles33, Imperial College London34, University of Innsbruck35, Harvard University36, University of Chicago37, University of Pittsburgh38, University of Notre Dame39, University of California, Berkeley40, Johns Hopkins University41, University of Bristol42, University of New South Wales43, Dartmouth College44, Whitman College45, University of Puerto Rico46, University of Milan47, University of California, Irvine48, Paris Dauphine University49, University of British Columbia50, Ludwig Maximilian University of Munich51, Purdue University52, Washington University in St. Louis53, University of California, Davis54, Microsoft55
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.

1,586 citations

Posted Content
TL;DR: This article proposed to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005, which is the threshold used in this paper.
Abstract: We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.

1,415 citations

Journal ArticleDOI
TL;DR: In this paper, a mixture of g priors is proposed as an alternative to the default g prior, which resolves many of the problems with the original formulation while maintaining the computational tractability of the g prior.
Abstract: Zellner's g prior remains a popular conventional prior for use in Bayesian variable selection, despite several undesirable consistency issues. In this article we study mixtures of g priors as an alternative to default g priors that resolve many of the problems with the original formulation while maintaining the computational tractability that has made the g prior so popular. We present theoretical properties of the mixture g priors and provide real and simulated examples to compare the mixture formulation with fixed g priors, empirical Bayes approaches, and other default procedures. Please see Arnold Zellner's letter and the author's response.

1,115 citations


Cited by
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Book
Vladimir Vapnik1
01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

40,147 citations

Proceedings Article
03 Jan 2001
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Abstract: We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

25,546 citations

Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Journal ArticleDOI
TL;DR: A computationally feasible method for finding such maximum likelihood estimates is developed, and a computer program is available that allows the testing of hypotheses about the constancy of evolutionary rates by likelihood ratio tests.
Abstract: The application of maximum likelihood techniques to the estimation of evolutionary trees from nucleic acid sequence data is discussed. A computationally feasible method for finding such maximum likelihood estimates is developed, and a computer program is available. This method has advantages over the traditional parsimony algorithms, which can give misleading results if rates of evolution differ in different lineages. It also allows the testing of hypotheses about the constancy of evolutionary rates by likelihood ratio tests, and gives rough indication of the error of the estimate of the tree.

13,111 citations

Book
21 Mar 2002
TL;DR: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced.
Abstract: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

9,509 citations