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Journal of the American Statistical Association Volume 104, 2009 - Issue 487
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Theory and Methods

Constructing Confidence Regions of Optimal Expected Size

Chad M. Schafer Chad M. Schafer is Assistant Professor, Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213 . Philip B. Stark is Professor, Department of Statistics, University of California, Berkeley, CA 94720 . This work was supported by NSF Grants #9872979 and #0434343, and by the AX Division at the Lawrence Livermore National Laboratory through the Department of Energy under contract W-7405-Eng-48. The authors thank the referees for many helpful comments.
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Philip B. Stark Chad M. Schafer is Assistant Professor, Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213 . Philip B. Stark is Professor, Department of Statistics, University of California, Berkeley, CA 94720 . This work was supported by NSF Grants #9872979 and #0434343, and by the AX Division at the Lawrence Livermore National Laboratory through the Department of Energy under contract W-7405-Eng-48. The authors thank the referees for many helpful comments.
Pages 1080-1089 | Received 01 Aug 2007, Published online: 01 Jan 2012

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Christian Bartels, Johanna Mielke & Ekkehard Glimm. (2022) Confidence intervals with maximal average power. Communications in Statistics - Theory and Methods 51:14, pages 4940-4956.
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Ludwig Baringhaus & Daniel Gaigall. (2019) On an asymptotic relative efficiency concept based on expected volumes of confidence regions. Statistics 53:6, pages 1396-1436.
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Articles from other publishers (9)

Hongxiang Qiu & Alex Luedtke. (2023) Adversarial meta-learning of Gamma-minimax estimators that leverage prior knowledge. Electronic Journal of Statistics 17:2.
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James Carzon, Bruno Abreu, Leighton Regayre, Kenneth Carslaw, Lucia Deaconu, Philip Stier, Hamish Gordon & Mikael Kuusela. (2023) Statistical constraints on climate model parameters using a scalable cloud-based inference framework. Environmental Data Science 2.
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Michael Stanley, Pratik Patil & Mikael Kuusela. (2022) Uncertainty quantification for wide-bin unfolding: one-at-a-time strict bounds and prior-optimized confidence intervals. Journal of Instrumentation 17:10, pages P10013.
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Paul D. Baines, Xiao-Li Meng, Andreas Zezas & Vinay Kashyap. 2022. Statistics in the Public Interest. Statistics in the Public Interest 53 82 .
Alex Luedtke, Marco Carone, Noah SimonOleg Sofrygin. (2020) Learning to learn from data: Using deep adversarial learning to construct optimal statistical procedures. Science Advances 6:9.
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Philip B. Stark. (2015) Constraints versus Priors. SIAM/ASA Journal on Uncertainty Quantification 3:1, pages 586-598.
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Joshua D. Habiger, Melinda H. McCann & Joshua M. Tebbs. (2013) On optimal confidence sets for parameters in discrete distributions. Statistics & Probability Letters 83:1, pages 297-303.
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Chad M. Schafer & Peter E. Freeman. 2012. Statistical Challenges in Modern Astronomy V. Statistical Challenges in Modern Astronomy V 3 19 .
P. B. Stark & L. Tenorio. 2010. Large‐Scale Inverse Problems and Quantification of Uncertainty. Large‐Scale Inverse Problems and Quantification of Uncertainty 9 32 .

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