IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v165y2022ics0167947321001547.html
   My bibliography  Save this article

Optimal designs for order-of-addition experiments

Author

Listed:
  • Zhao, Yuna
  • Lin, Dennis K.J.
  • Liu, Min-Qian

Abstract

The order-of-addition (OofA) designs have received significant attention over recent years. It is of great interest to seek for efficient fractional OofA designs especially when the number of components is large. It has been recognized that constructing efficient fractional OofA designs is a challenging work. A systematic construction method for a class of efficient fractional OofA designs, called OofA orthogonal arrays (OofA-OAs), is proposed. It is shown that OofA-OAs are superior over any other type of fractional OofA designs for the predominant pair-wise ordering (PWO) model. The balance property of OofA-OAs is also developed. In addition, the capacity of OofA-OAs for estimating different models is investigated.

Suggested Citation

  • Zhao, Yuna & Lin, Dennis K.J. & Liu, Min-Qian, 2022. "Optimal designs for order-of-addition experiments," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
  • Handle: RePEc:eee:csdana:v:165:y:2022:i:c:s0167947321001547
    DOI: 10.1016/j.csda.2021.107320
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947321001547
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2021.107320?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. Chen, Jianbin & Mukerjee, Rahul & Lin, Dennis K.J., 2020. "Construction of optimal fractional Order-of-Addition designs via block designs," Statistics & Probability Letters, Elsevier, vol. 161(C).
    2. Xiao, Qian & Xu, Hongquan, 2021. "A mapping-based universal Kriging model for order-of-addition experiments in drug combination studies," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    3. Yuna Zhao & Dennis K. J. Lin & Min-Qian Liu, 2021. "Designs for order-of-addition experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(8), pages 1475-1495, 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. Shengli Zhao & Zehui Dong & Yuna Zhao, 2022. "Order-of-Addition Orthogonal Arrays with High Strength," Mathematics, MDPI, vol. 10(7), pages 1-17, April.
    2. Wang, Xiaodi & Huang, Hengzhen, 2023. "Group symmetric Latin hypercube designs for symmetrical global sensitivity analysis," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    3. Zabihinia Gerdroodbari, Yasin & Khorasany, Mohsen & Razzaghi, Reza & Heidari, Rahmat, 2024. "Management of prosumers using dynamic export limits and shared Community Energy Storage," Applied Energy, Elsevier, vol. 355(C).
    4. Dongying Wang & Sumin Wang, 2023. "Constructing Optimal Designs for Order-of-Addition Experiments Using a Hybrid Algorithm," Mathematics, MDPI, vol. 11(11), pages 1-20, May.

    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. Rios, Nicholas & Winker, Peter & Lin, Dennis K.J., 2022. "TA algorithms for D-optimal OofA Mixture designs," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    2. Shengli Zhao & Zehui Dong & Yuna Zhao, 2022. "Order-of-Addition Orthogonal Arrays with High Strength," Mathematics, MDPI, vol. 10(7), pages 1-17, April.
    3. Kassie, Girma T. & Abdulai, Awudu & Haile, Aynalem & Yitayih, Mulugeta & Asnake, Woinishet & Rischkowsky, Barbara, 2023. "Understanding pastoralists’ preferences for goat traits: Application of all-levels and end-point choice experiments," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 104(C).
    4. Dongying Wang & Sumin Wang, 2023. "Constructing Optimal Designs for Order-of-Addition Experiments Using a Hybrid Algorithm," Mathematics, MDPI, vol. 11(11), pages 1-20, May.
    5. Ahmed Selema & Mohamed N. Ibrahim & Peter Sergeant, 2022. "Metal Additive Manufacturing for Electrical Machines: Technology Review and Latest Advancements," Energies, MDPI, vol. 15(3), pages 1-18, January.

    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:csdana:v:165:y:2022:i:c:s0167947321001547. 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/locate/csda .

    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.