IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v57y2016i4d10.1007_s00362-016-0809-0.html
   My bibliography  Save this article

Optimal approximate designs for estimating treatment contrasts resistant to nuisance effects

Author

Listed:
  • Samuel Rosa

    (Comenius University in Bratislava)

  • Radoslav Harman

    (Comenius University in Bratislava)

Abstract

Suppose that we intend to perform an experiment consisting of a set of independent trials. The mean value of the response in each trial is assumed to be equal to the sum of the effect of the treatment selected for that trial and some nuisance effects, e.g., the effect of a time trend or blocking. In this model, we examine optimal approximate designs for the estimation of a system of treatment contrasts, with respect to a wide range of optimality criteria. We show that it is necessary for any optimal design to attain the optimal treatment proportions, which may be obtained from the marginal model that excludes the nuisance effects. Moreover, we prove that for a design to be optimal, it is sufficient that it attains the optimal treatment proportions and satisfies the conditions for resistance to nuisance effects. For selected natural choices of treatment contrasts and optimality criteria, we calculate the optimal treatment proportions and provide an explicit form of optimal designs. In particular, we obtain optimal treatment proportions for the comparison of a set of test treatments with a set of controls. Once the optimal treatment proportions are determined, the results allow us to construct a method of calculating optimal approximate designs with small support sizes through linear programming. Consequently, we can construct efficient exact designs using a simple heuristic.

Suggested Citation

  • Samuel Rosa & Radoslav Harman, 2016. "Optimal approximate designs for estimating treatment contrasts resistant to nuisance effects," Statistical Papers, Springer, vol. 57(4), pages 1077-1106, December.
  • Handle: RePEc:spr:stpapr:v:57:y:2016:i:4:d:10.1007_s00362-016-0809-0
    DOI: 10.1007/s00362-016-0809-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-016-0809-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00362-016-0809-0?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. Katarzyna Filipiak & Augustyn Markiewicz & Anna Szczepańska, 2009. "Optimal designs under a multivariate linear model with additional nuisance parameters," Statistical Papers, Springer, vol. 50(4), pages 761-778, August.
    2. Radoslav Harman, 2004. "Minimal efficiency of designs under the class of orthogonally invariant information criteria," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 60(2), pages 137-153, September.
    3. Mike Jacroux, 1990. "Some optimal designs for comparing a set of test treatments with a set of controls," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(1), pages 173-185, March.
    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. Rosa, Samuel & Harman, Radoslav, 2022. "Computing minimum-volume enclosing ellipsoids for large datasets," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    2. Harman, Radoslav & Rosa, Samuel, 2019. "Removal of the points that do not support an E-optimal experimental design," Statistics & Probability Letters, Elsevier, vol. 147(C), pages 83-89.
    3. Samuel Rosa, 2018. "Optimal designs for treatment comparisons represented by graphs," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 479-503, October.
    4. Samuel Rosa, 2019. "Equivalence of weighted and partial optimality of experimental designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(6), pages 719-732, August.
    5. Samuel Rosa & Radoslav Harman, 2017. "Optimal approximate designs for comparison with control in dose-escalation studies," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 638-660, September.

    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. Anna Szczepańska, 2013. "Simultaneous choice of time points and the block design in the growth curve model," Statistical Papers, Springer, vol. 54(2), pages 413-425, May.
    2. 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.
    3. Samuel Rosa, 2018. "Optimal designs for treatment comparisons represented by graphs," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 479-503, October.
    4. Katarína Burclová & Andrej Pázman, 2016. "Optimal design of experiments via linear programming," Statistical Papers, Springer, vol. 57(4), pages 893-910, December.
    5. Seema Jaggi & Kader Ali Sarkar & Arpan Bhowmik & Eldho Varghese & Cini Varghese & Anindita Datta, 2023. "Trend resistant balanced bipartite block designs," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 211-235, March.
    6. Katarzyna Filipiak & Dietrich Rosen, 2012. "On MLEs in an extended multivariate linear growth curve model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1069-1092, November.

    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:spr:stpapr:v:57:y:2016:i:4:d:10.1007_s00362-016-0809-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.