IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v84y2016i3p487-505.html
   My bibliography  Save this article

H-likelihood Predictive Intervals for Unobservables

Author

Listed:
  • Youngjo Lee
  • Gwangsu Kim

Abstract

No abstract is available for this item.

Suggested Citation

  • Youngjo Lee & Gwangsu Kim, 2016. "H-likelihood Predictive Intervals for Unobservables," International Statistical Review, International Statistical Institute, vol. 84(3), pages 487-505, December.
  • Handle: RePEc:bla:istatr:v:84:y:2016:i:3:p:487-505
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/insr.12115
    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. Renjun Ma & Bent Jørgensen, 2007. "Nested generalized linear mixed models: an orthodox best linear unbiased predictor approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 625-641, September.
    2. Edward J. Bedrick & Joe R. Hill, 1999. "Properties and Applications of the Generalized Likelihood as a Summary Function for Prediction Problems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(4), pages 593-609, December.
    3. Min-ge Xie & Kesar Singh, 2013. "Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review," International Statistical Review, International Statistical Institute, vol. 81(1), pages 3-39, April.
    4. Youngjo Lee & Jan F. Bjørnstad, 2013. "Extended likelihood approach to large-scale multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 553-575, June.
    5. Anders Skrondal & Sophia Rabe‐Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745, December.
    Full references (including those not matched with items on IDEAS)

    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. Lee Youngjo & Gwangsu Kim, 2020. "Properties of h‐Likelihood Estimators in Clustered Data," International Statistical Review, International Statistical Institute, vol. 88(2), pages 380-395, August.
    2. Delbianco Fernando & Tohmé Fernando, 2023. "What is a relevant control?: An algorithmic proposal," Asociación Argentina de Economía Política: Working Papers 4643, Asociación Argentina de Economía Política.
    3. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "GLLAMM Manual," U.C. Berkeley Division of Biostatistics Working Paper Series 1160, Berkeley Electronic Press.
    4. Arpino, Bruno & Varriale, Roberta, 2009. "Assessing the quality of institutions’ rankings obtained through multilevel linear regression models," MPRA Paper 19873, University Library of Munich, Germany.
    5. Bigoni, Maria & Fort, Margherita, 2013. "Information and learning in oligopoly: An experiment," Games and Economic Behavior, Elsevier, vol. 81(C), pages 192-214.
    6. Carter, Virginia & Derudder, Ben & Henríquez, Cristián, 2021. "Assessing local governments’ perception of the potential implementation of biophilic urbanism in Chile: A latent class approach," Land Use Policy, Elsevier, vol. 101(C).
    7. Guang Yang & Dungang Liu & Junyuan Wang & Min‐ge Xie, 2016. "Meta‐analysis framework for exact inferences with application to the analysis of rare events," Biometrics, The International Biometric Society, vol. 72(4), pages 1378-1386, December.
    8. Matteo Bottai & Nicola Orsini, 2004. "Confidence intervals for the variance component of random-effects linear models," Stata Journal, StataCorp LP, vol. 4(4), pages 429-435, December.
    9. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2023. "A higher-order correct fast moving-average bootstrap for dependent data," Journal of Econometrics, Elsevier, vol. 235(1), pages 65-81.
    10. M. Tariqul Hasan & Gary Sneddon & Renjun Ma, 2012. "Regression analysis of zero-inflated time-series counts: application to air pollution related emergency room visit data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 467-476, June.
    11. Alcácer, Juan & Gittelman, Michelle & Sampat, Bhaven, 2009. "Applicant and examiner citations in U.S. patents: An overview and analysis," Research Policy, Elsevier, vol. 38(2), pages 415-427, March.
    12. Villena, Mauricio G. & Zecchetto, Franco, 2011. "Subject-specific performance information can worsen the tragedy of the commons: Experimental evidence," Journal of Economic Psychology, Elsevier, vol. 32(3), pages 330-347, June.
    13. Xiaokang Luo & Tirthankar Dasgupta & Minge Xie & Regina Y. Liu, 2021. "Leveraging the Fisher randomization test using confidence distributions: Inference, combination and fusion learning," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 777-797, September.
    14. Bigoni, Maria & Le Coq, Chloé & Fridolfsson, Sven-Olof & Spagnolo, Giancarlo, 2008. "Risk Aversion, Prospect Theory, and Strategic Risk in Law Enforcement: Evidence From an Antitrust Experiment," SSE/EFI Working Paper Series in Economics and Finance 696, Stockholm School of Economics.
    15. Getinet A. Haile, 2015. "Workplace Job Satisfaction in Britain: Evidence from Linked Employer–Employee Data," LABOUR, CEIS, vol. 29(3), pages 225-242, September.
    16. Peter Haan, 2006. "Slowly, but Changing: How Does Genuine State Dependence Affect Female Labor Supply on the Extensive and Intensive Margin," JEPS Working Papers 06-002, JEPS.
    17. Ann Shawing Yang, 2015. "Lottery Payment Cards: A Study of Mental Accounting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(3), pages 201-226, July.
    18. Zhao, Xiujie & Chen, Piao & Gaudoin, Olivier & Doyen, Laurent, 2021. "Accelerated degradation tests with inspection effects," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1099-1114.
    19. Erlis Ruli & Laura Ventura, 2021. "Can Bayesian, confidence distribution and frequentist inference agree?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 359-373, March.
    20. Piero Veronese & Eugenio Melilli, 2021. "Confidence Distribution for the Ability Parameter of the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 131-166, March.

    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:istatr:v:84:y:2016:i:3:p:487-505. 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: https://edirc.repec.org/data/isiiinl.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.