IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v73y2017i1p242-251.html
   My bibliography  Save this article

Adaptive prior weighting in generalized regression

Author

Listed:
  • Leonhard Held
  • Rafael Sauter

Abstract

No abstract is available for this item.

Suggested Citation

  • Leonhard Held & Rafael Sauter, 2017. "Adaptive prior weighting in generalized regression," Biometrics, The International Biometric Society, vol. 73(1), pages 242-251, March.
  • Handle: RePEc:bla:biomet:v:73:y:2017:i:1:p:242-251
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12541
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Heinz Schmidli & Sandro Gsteiger & Satrajit Roychoudhury & Anthony O'Hagan & David Spiegelhalter & Beat Neuenschwander, 2014. "Robust meta-analytic-predictive priors in clinical trials with historical control information," Biometrics, The International Biometric Society, vol. 70(4), pages 1023-1032, December.
    2. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    3. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    4. Martins, Thiago G. & Simpson, Daniel & Lindgren, Finn & Rue, Håvard, 2013. "Bayesian computing with INLA: New features," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 68-83.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. repec:dau:papers:123456789/1906 is not listed on IDEAS
    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. Danila Azzolina & Paola Berchialla & Dario Gregori & Ileana Baldi, 2021. "Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    2. Mani Suleiman & Haydar Demirhan & Leanne Boyd & Federico Girosi & Vural Aksakalli, 2019. "Bayesian logistic regression approaches to predict incorrect DRG assignment," Health Care Management Science, Springer, vol. 22(2), pages 364-375, June.

    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. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
    2. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    3. Arnab Kumar Maity & Sanjib Basu & Santu Ghosh, 2021. "Bayesian criterion‐based variable selection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 835-857, August.
    4. Carson, Stuart & Mills Flemming, Joanna, 2014. "Seal encounters at sea: A contemporary spatial approach using R-INLA," Ecological Modelling, Elsevier, vol. 291(C), pages 175-181.
    5. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
    6. Mulder, Joris, 2014. "Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 448-463.
    7. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    8. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    9. David Jiménez-Hernández & Víctor González-Calatayud & Ana Torres-Soto & Asunción Martínez Mayoral & Javier Morales, 2020. "Digital Competence of Future Secondary School Teachers: Differences According to Gender, Age, and Branch of Knowledge," Sustainability, MDPI, vol. 12(22), pages 1-16, November.
    10. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
    11. V. A. Alegana & C. Pezzulo & A. J. Tatem & B. Omar & A. Christensen, 2021. "Mapping out-of-school adolescents and youths in low- and middle-income countries," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-10, December.
    12. Dimitris Korobilis, 2020. "Sign restrictions in high-dimensional vector autoregressions," Working Papers 2020_21, Business School - Economics, University of Glasgow.
    13. Korobilis, Dimitris, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, vol. 148(C).
    14. Debamita Kundu & Riten Mitra & Jeremy T. Gaskins, 2021. "Bayesian variable selection for multioutcome models through shared shrinkage," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 295-320, March.
    15. Andrew J. Womack & Luis León-Novelo & George Casella, 2014. "Inference From Intrinsic Bayes' Procedures Under Model Selection and Uncertainty," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1040-1053, September.
    16. Delle-Monache, Davide & De-Polis, Andrea & Petrella, Ivan, 2020. "Modelling and Forecasting Macroeconomic Downside Risk," EMF Research Papers 34, Economic Modelling and Forecasting Group.
    17. Florian Huber & Michael Pfarrhofer, 2021. "Dynamic shrinkage in time‐varying parameter stochastic volatility in mean models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(2), pages 262-270, March.
    18. Chrisovalantis Malesios & Antonis Skouloudis & Prasanta Kumar Dey & Fouad Ben Abdelaziz & Apostolos Kantartzis & Konstantinos Evangelinos, 2018. "Impact of small‐ and medium‐sized enterprises sustainability practices and performance on economic growth from a managerial perspective: Modeling considerations and empirical analysis results," Business Strategy and the Environment, Wiley Blackwell, vol. 27(7), pages 960-972, November.
    19. Luca Grassetti & Laura Rizzi, 2019. "The determinants of individual health care expenditures in the Italian region of Friuli Venezia Giulia: evidence from a hierarchical spatial model estimation," Empirical Economics, Springer, vol. 56(3), pages 987-1009, March.
    20. Ferreira, Marco A.R. & Porter, Erica M. & Franck, Christopher T., 2021. "Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).

    More about this item

    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:bla:biomet:v:73:y:2017:i:1:p:242-251. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.