IDEAS home Printed from https://ideas.repec.org/p/hhs/oruesi/2021_007.html
   My bibliography  Save this paper

Bayesian model selection: Application to adjustment of fundamental physical constants

Author

Listed:

Abstract

The location-scale model is usually present in physics and chemistry in connection to the Birge ratio method for the adjustment of fundamental physical constants such as the Planck constant or the Newtonian constant of gravitation, while the random effects model is the commonly used approach for meta-analysis in medicine. These two competitive models are used to increase the quoted uncertainties of the measurement results to make them consistent. The intrinsic Bayes factor (IBF) is derived for the comparison of the random effects model to the location-scale model, and we answer the question which model performs better for the determination of the Newtonian constant of gravitation. The results of the empirical illustration support the application of the Birge ratio method which is currently used in the adjustment of the CODATA 2018 value for the Newtonian constant of gravitation together with its uncertainty. The results of the simulation study illustrate that the suggested procedure for model selection is decisive even when data consist of a few measurement results.

Suggested Citation

  • Bodnar, Olha & Eriksson, Viktor, 2021. "Bayesian model selection: Application to adjustment of fundamental physical constants," Working Papers 2021:7, Örebro University, School of Business.
    • Handle: RePEc:hhs:oruesi:2021_007
    as

    Download full text from publisher

    File URL: https://www.oru.se/globalassets/oru-sv/institutioner/hh/workingpapers/workingpapers2021/wp-7-2021.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. S. Alighanbari & G. S. Giri & F. L. Constantin & V. I. Korobov & S. Schiller, 2020. "Precise test of quantum electrodynamics and determination of fundamental constants with HD+ ions," Nature, Nature, vol. 581(7807), pages 152-158, May.
    2. Fernández, Carmen & Steel, Mark F. J., 1999. "Reference priors for the general location-scale modelm," Statistics & Probability Letters, Elsevier, vol. 43(4), pages 377-384, July.
    3. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
    4. A. E. Ades & G. Lu & J. P. T. Higgins, 2005. "The Interpretation of Random-Effects Meta-Analysis in Decision Models," Medical Decision Making, , vol. 25(6), pages 646-654, November.
    5. Andrew L. Rukhin, 2013. "Estimating heterogeneity variance in meta-analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 451-469, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bodnar, Olha, 2021. "Bayesian Model Selection for Small Datasets of Measurement Results," Working Papers 2021:6, Örebro University, School of Business.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bodnar, Olha & Bodnar, Taras, 2021. "Objective Bayesian meta-analysis based on generalized multivariate random effects model," Working Papers 2021:5, Örebro University, School of Business.
    2. Zhiqing Zhao & Faying Lin & Bennett Wang & Yihai Cao & Xu Hou & Yangang Wang, 2016. "Residential Proximity to Major Roadways and Risk of Type 2 Diabetes Mellitus: A Meta-Analysis," IJERPH, MDPI, vol. 14(1), pages 1-12, December.
    3. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    4. Sofia Dias & Alex J. Sutton & Nicky J. Welton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 3," Medical Decision Making, , vol. 33(5), pages 618-640, July.
    5. Morris, Tim P & White, Ian R & Pham, Tra My & Quartagno, Matteo, 2023. "How to check a simulation study," OSF Preprints cbr72, Center for Open Science.
    6. Nelson, Jon Paul, 2020. "Fixed-effect versus random-effects meta-analysis in economics: A study of pass-through rates for alcohol beverage excise taxes," Economics Discussion Papers 2020-1, Kiel Institute for the World Economy (IfW Kiel).
    7. Ibrahim Y. Tawbe, 2023. "Environmental disclosure programs and birth weight: a meta- analysis," Working Papers 2023-02, CRESE.
    8. Fanjie Meng & Xiangpo Pan & Wenzhen Tong, 2018. "Rifampicin versus streptomycin for brucellosis treatment in humans: A meta-analysis of randomized controlled trials," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-12, February.
    9. Biao Zhou & Gao Feng Feng Cai & Hua Kun Kun Lv & Shuang Fei Fei Xu & Zheng Ting Ting Wang & Zheng Gang Gang Jiang & Chong Gao Gao Hu & Yong Di Di Chen, 2019. "Factors Correlating to the Development of Hepatitis C Virus Infection among Drug Users—Findings from a Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 16(13), pages 1-17, July.
    10. Alberto Aiolfi & Emanuele Asti & Emanuele Rausa & Giulia Bonavina & Gianluca Bonitta & Luigi Bonavina, 2018. "Use of C-reactive protein for the early prediction of anastomotic leak after esophagectomy: Systematic review and Bayesian meta-analysis," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-13, December.
    11. Cebiroglu, Gökhan & Hautsch, Nikolaus & Walsh, Christopher, 2019. "Revisiting the stealth trading hypothesis: Does time-varying liquidity explain the size-effect?," CFS Working Paper Series 625, Center for Financial Studies (CFS).
    12. Bijing Mao & Yafei Li & Zhimin Zhang & Chuan Chen & Yuanyuan Chen & Chenchen Ding & Lin Lei & Jian Li & Mei Jiang & Dong Wang & Ge Wang, 2015. "One-Carbon Metabolic Factors and Risk of Renal Cell Cancer: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-10, October.
    13. Soumya Roy & Biswabrata Pradhan, 2023. "Inference for log‐location‐scale family of distributions under competing risks with progressive type‐I interval censored data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 208-232, May.
    14. Bodnar, Olha & Bodnar, Taras, 2024. "Gibbs sampler approach for objective Bayesian inference in elliptical multivariate meta-analysis random effects model," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    15. Shefali Liyanage & Kiran Saqib & Amber Fozia Khan & Tijhiana Rose Thobani & Wang-Choi Tang & Cameron B. Chiarot & Bara’ Abdallah AlShurman & Zahid Ahmad Butt, 2021. "Prevalence of Anxiety in University Students during the COVID-19 Pandemic: A Systematic Review," IJERPH, MDPI, vol. 19(1), pages 1-13, December.
    16. Richard Grieve & Richard Nixon & Simon G. Thompson & John Cairns, 2007. "Multilevel models for estimating incremental net benefits in multinational studies," Health Economics, John Wiley & Sons, Ltd., vol. 16(8), pages 815-826, August.
    17. Schmidli, Heinz & Neuenschwander, Beat & Friede, Tim, 2017. "Meta-analytic-predictive use of historical variance data for the design and analysis of clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 100-110.
    18. Se Young Kim & Mi-Kyoung Cho, 2022. "The Effect of Nurse Support Programs on Job Satisfaction and Organizational Behaviors among Hospital Nurses: A Meta-Analysis," IJERPH, MDPI, vol. 19(24), pages 1-23, December.
    19. Layan Sukik & Maryam Alyafei & Manale Harfouche & Laith J Abu-Raddad, 2019. "Herpes simplex virus type 1 epidemiology in Latin America and the Caribbean: Systematic review and meta-analytics," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-20, April.
    20. Vladislav Morozov, 2022. "Inference on Extreme Quantiles of Unobserved Individual Heterogeneity," Papers 2210.08524, arXiv.org, revised Jun 2023.

    More about this item

    Keywords

    Intrinsic Bayes factor; Birge ratio method; Location-scale model; Random-e ects model; Reference prior; Meta-analysis; Interlaboratory comparison study; Newtonian constant of gravitation;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:oruesi:2021_007. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/ieoruse.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.