IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v159y2020ics016771521930330x.html
   My bibliography  Save this article

R-optimal designs for individual prediction in random coefficient regression models

Author

Listed:
  • He, Lei
  • He, Daojiang

Abstract

In this paper we consider optimal designs for the R-criterion in random coefficient regression models. We derive two equivalence theorems to characterize the optimal designs for the prediction of the individual parameters and for the individual deviations from the unknown population mean parameters in random coefficient models. Some examples of the R-optimal designs for the straight line regression with random coefficients are presented for illustration.

Suggested Citation

  • He, Lei & He, Daojiang, 2020. "R-optimal designs for individual prediction in random coefficient regression models," Statistics & Probability Letters, Elsevier, vol. 159(C).
  • Handle: RePEc:eee:stapro:v:159:y:2020:i:c:s016771521930330x
    DOI: 10.1016/j.spl.2019.108684
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016771521930330X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.spl.2019.108684?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Maryna Prus, 2019. "Optimal designs for minimax-criteria in random coefficient regression models," Statistical Papers, Springer, vol. 60(2), pages 465-478, April.
    2. Thomas Schmelter, 2007. "The Optimality of Single-group Designs for Certain Mixed Models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 65(2), pages 183-193, February.
    3. Xin Liu & Rong-Xian Yue & Weng Kee Wong, 2019. "D-optimal designs for multi-response linear mixed models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(1), pages 87-98, January.
    4. Maryna Prus & Rainer Schwabe, 2016. "Optimal designs for the prediction of individual parameters in hierarchical models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 175-191, January.
    5. Holger Dette, 1997. "Designing Experiments with Respect to ‘Standardized’ Optimality Criteria," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 97-110.
    6. Liu, Xin & Yue, Rong-Xian & Chatterjee, Kashinath, 2014. "R-optimal designs in random coefficient regression models," Statistics & Probability Letters, Elsevier, vol. 88(C), pages 127-132.
    7. Prus, Maryna, 2019. "Various optimality criteria for the prediction of individual response curves," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 36-41.
    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. Xin Liu & Rong‐Xian Yue & Weng Kee Wong, 2022. "Equivalence theorems for c and DA‐optimality for linear mixed effects models with applications to multitreatment group assignments in health care," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1842-1859, December.

    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. Xin Liu & Rong‐Xian Yue & Weng Kee Wong, 2022. "Equivalence theorems for c and DA‐optimality for linear mixed effects models with applications to multitreatment group assignments in health care," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1842-1859, December.
    2. Liu, Xin & Ye, Min & Yue, Rong-Xian, 2021. "Optimal designs for comparing population curves in hierarchical models," Statistics & Probability Letters, Elsevier, vol. 178(C).
    3. Lei He & Rong-Xian Yue, 2021. "D-optimal designs for hierarchical linear models with intraclass covariance structure," Statistical Papers, Springer, vol. 62(3), pages 1349-1361, June.
    4. Maryna Prus & Hans-Peter Piepho, 2021. "Optimizing the Allocation of Trials to Sub-regions in Multi-environment Crop Variety Testing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 267-288, June.
    5. Liu, Xin & Yue, Rong-Xian & Chatterjee, Kashinath, 2020. "Geometric characterization of D-optimal designs for random coefficient regression models," Statistics & Probability Letters, Elsevier, vol. 159(C).
    6. Renata Eirini Tsirpitzi & Frank Miller & Carl-Fredrik Burman, 2023. "Robust optimal designs using a model misspecification term," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(7), pages 781-804, October.
    7. Liu, Xin & Yue, Rong-Xian & Chatterjee, Kashinath, 2014. "R-optimal designs in random coefficient regression models," Statistics & Probability Letters, Elsevier, vol. 88(C), pages 127-132.
    8. Cheng, Jing & Ai, Mingyao, 2020. "Optimal designs for panel data linear regressions," Statistics & Probability Letters, Elsevier, vol. 163(C).
    9. Prus, Maryna, 2023. "Optimal designs for prediction of random effects in two-groups models with multivariate response," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    10. Hao, Honghua & Zhu, Xiaoyuan & Zhang, Xinfeng & Zhang, Chongqi, 2021. "R-optimal design of the second-order Scheffé mixture model," Statistics & Probability Letters, Elsevier, vol. 173(C).
    11. Marius Schmidt, 2023. "Standardized maximin D- and c-optimal designs for the Poisson–Gamma model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(6), pages 697-721, August.
    12. Maryna Prus, 2019. "Optimal designs for minimax-criteria in random coefficient regression models," Statistical Papers, Springer, vol. 60(2), pages 465-478, April.
    13. Dennis Schmidt & Rainer Schwabe, 2015. "On optimal designs for censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(3), pages 237-257, April.
    14. Lenka Filová & Mária Trnovská & Radoslav Harman, 2012. "Computing maximin efficient experimental designs using the methods of semidefinite programming," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(5), pages 709-719, July.
    15. Sanjoy Sinha, 2013. "Robust designs for multivariate logistic regression," METRON, Springer;Sapienza Università di Roma, vol. 71(2), pages 157-173, September.
    16. Dette, Holger & O'Brien, Timothy E., 2003. "Efficient experimental design for the Behrens-Fisher problem with application to bioassay," Technical Reports 2003,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    17. Xin Liu & Rong-Xian Yue & Weng Kee Wong, 2019. "D-optimal designs for multi-response linear mixed models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(1), pages 87-98, January.
    18. Harman, Radoslav & Jurík, Tomás, 2008. "Computing c-optimal experimental designs using the simplex method of linear programming," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 247-254, December.
    19. Dette, Holger & Melas, Viatcheslav B. & Pepelyshev, Andrey, 2006. "Optimal designs for free knot least squares splines," Technical Reports 2006,34, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    20. Li, Guanghui & Zhang, Chongqi, 2017. "The pseudo component transformation design for experiment with mixture," Statistics & Probability Letters, Elsevier, vol. 131(C), pages 19-24.

    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:eee:stapro:v:159:y:2020:i:c:s016771521930330x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.