IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v106y2015icp58-64.html
   My bibliography  Save this article

Construction of main-effect plans orthogonal through the block factor

Author

Listed:
  • Chen, Xue-Ping
  • Lin, Jin-Guan
  • Yang, Jian-Feng
  • Wang, Hong-Xia

Abstract

Bagchi (2010) proposed main-effect plans orthogonal through the block factor (POTB), in which the treatment factors are pairwise orthogonal through the block factor. However, not many construction methods are available in the literature. In this paper, we present several new construction approaches for saturated POTBs with small runs and mixed levels. Moreover, all of them are connected and variance-balanced.

Suggested Citation

  • Chen, Xue-Ping & Lin, Jin-Guan & Yang, Jian-Feng & Wang, Hong-Xia, 2015. "Construction of main-effect plans orthogonal through the block factor," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 58-64.
    • Handle: RePEc:eee:stapro:v:106:y:2015:i:c:p:58-64
    DOI: 10.1016/j.spl.2015.06.029
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715215002370
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2015.06.029?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. Bose, Mausumi & Bagchi, Sunanda, 2007. "Optimal main effect plans in blocks of small size," Statistics & Probability Letters, Elsevier, vol. 77(2), pages 142-147, January.
    2. Yang, Jianfeng & Sun, Fasheng & Lin, Dennis K.J. & Liu, Min-Qian, 2010. "A study on design uniformity under errors in the level values," Statistics & Probability Letters, Elsevier, vol. 80(19-20), pages 1467-1471, October.
    3. Rahul Mukerjee, 2002. "Optimal main effect plans with non-orthogonal blocking," Biometrika, Biometrika Trust, vol. 89(1), pages 225-229, March.
    4. Yong-Dao Zhou & Hongquan Xu, 2014. "Space-Filling Fractional Factorial Designs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1134-1144, September.
    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. Wang, Xiaodi & Chen, Xueping & Zhang, Yingshan, 2018. "Feasible blocked multi-factor designs of unequal block sizes," Statistics & Probability Letters, Elsevier, vol. 135(C), pages 102-109.

    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. Dutta, Ganesh & SahaRay, Rita, 2015. "D- and E-optimal blocked main effects plans with unequal block sizes when n is odd," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 37-43.
    2. Jacroux, Mike, 2013. "A note on the optimality of 2-level main effects plans in blocks of odd size," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1163-1166.
    3. Jacroux, Mike & Kealy-Dichone, Bonni, 2015. "On the type I optimality of blocked 2-level main effects plans having blocks of different sizes," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 39-43.
    4. Jacroux, Mike & Kealy-Dichone, Bonni, 2015. "On the use of blocked 2-level main effects plans having blocks of different sizes," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 362-368.
    5. Bagchi, Sunanda & Bose, Mausumi, 2008. "Optimal main effect plans in nested row-column set-up of small size," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2720-2724, November.
    6. A. M. Elsawah, 2021. "Multiple doubling: a simple effective construction technique for optimal two-level experimental designs," Statistical Papers, Springer, vol. 62(6), pages 2923-2967, December.
    7. Bochuan Jiang & Yaping Wang & Mingyao Ai, 2022. "Search for minimum aberration designs with uniformity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 271-287, April.
    8. Zong-Feng Qi & Xue-Ru Zhang & Yong-Dao Zhou, 2018. "Generalized good lattice point sets," Computational Statistics, Springer, vol. 33(2), pages 887-901, June.
    9. A. M. Elsawah & Kai-Tai Fang & Xiao Ke, 2021. "New recommended designs for screening either qualitative or quantitative factors," Statistical Papers, Springer, vol. 62(1), pages 267-307, February.
    10. Liuping Hu & Kashinath Chatterjee & Jiaqi Liu & Zujun Ou, 2020. "New lower bound for Lee discrepancy of asymmetrical factorials," Statistical Papers, Springer, vol. 61(4), pages 1763-1772, August.
    11. Yang, Xue & Yang, Gui-Jun & Su, Ya-Juan, 2018. "Uniform minimum moment aberration designs," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 26-33.
    12. Zujun Ou & Minghui Zhang & Hongyi Li, 2023. "Triple Designs: A Closer Look from Indicator Function," Mathematics, MDPI, vol. 11(3), pages 1-12, February.
    13. Jacroux, Mike & Kealy-Dichone, Bonni, 2014. "On the E-optimality of blocked main effects plans when n≡3 (mod 4)," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 143-148.
    14. Wang, Sumin & Wang, Dongying & Sun, Fasheng, 2019. "A central limit theorem for marginally coupled designs," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 168-174.
    15. Kang Wang & Zujun Ou & Jiaqi Liu & Hongyi Li, 2021. "Uniformity pattern of q-level factorials under mixture discrepancy," Statistical Papers, Springer, vol. 62(4), pages 1777-1793, August.
    16. Bochuan Jiang & Fei Wang & Yaping Wang, 2022. "Construction of uniform mixed-level designs through level permutations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(6), pages 753-770, August.
    17. 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).
    18. Liuping Hu & Zujun Ou & Hongyi Li, 2020. "Construction of four-level and mixed-level designs with zero Lee discrepancy," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(1), pages 129-139, January.
    19. Bose, Mausumi & Bagchi, Sunanda, 2007. "Optimal main effect plans in blocks of small size," Statistics & Probability Letters, Elsevier, vol. 77(2), pages 142-147, January.
    20. A. M. Elsawah & Hong Qin, 2016. "Asymmetric uniform designs based on mixture discrepancy," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2280-2294, September.

    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:106:y:2015:i:c:p:58-64. 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.