IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v30y2015i4p1231-1244.html
   My bibliography  Save this article

Optimal design generation: an approach based on discovery probability

Author

Listed:
  • Roberto Fontana

Abstract

Efficient algorithms for searching for optimal saturated designs for sampling experiments are widely available. They maximize a given efficiency measure (such as D-optimality) and provide an optimum design. Nevertheless, they do not guarantee a global optimal design. Indeed, they start from an initial random design and find a local optimal design. If the initial design is changed the optimum found will, in general, be different. A natural question arises. Should we stop at the design found or should we run the algorithm again in search of a better design? This paper uses very recent methods and software for discovery probability to support the decision to continue or stop the sampling. A software tool written in SAS has been developed. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Roberto Fontana, 2015. "Optimal design generation: an approach based on discovery probability," Computational Statistics, Springer, vol. 30(4), pages 1231-1244, December.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:4:p:1231-1244
    DOI: 10.1007/s00180-015-0562-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-015-0562-1
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-015-0562-1?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. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "A New Estimator of the Discovery Probability," Biometrics, The International Biometric Society, vol. 68(4), pages 1188-1196, December.
    2. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "A new estimator of the discovery probability," DEM Working Papers Series 007, University of Pavia, Department of Economics and Management.
    3. P. Angelopoulos & H. Evangelaras & C. Koukouvinos & E. Lappas, 2007. "An effective step-down algorithm for the construction and the identification of nonisomorphic orthogonal arrays," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 66(2), pages 139-149, September.
    4. Mauro Gasparini, 2012. "Mixtures and limits of symmetric random integer partitions," METRON, Springer;Sapienza Università di Roma, vol. 70(2), pages 207-217, August.
    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. Antonio Canale & Igor Prünster, 2017. "Robustifying Bayesian nonparametric mixtures for count data," Biometrics, The International Biometric Society, vol. 73(1), pages 174-184, March.
    2. Emanuele Dolera, 2022. "Asymptotic Efficiency of Point Estimators in Bayesian Predictive Inference," Mathematics, MDPI, vol. 10(7), pages 1-27, April.
    3. Julyan Arbel & Stefano Favaro, 2021. "Approximating Predictive Probabilities of Gibbs-Type Priors," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 496-519, February.
    4. Cesari, Oriana & Favaro, Stefano & Nipoti, Bernardo, 2014. "Posterior analysis of rare variants in Gibbs-type species sampling models," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 79-98.
    5. SCHOEN, Eric D. & VO-THANH, Nha & GOOS, Peter, 2015. "Two-level orthogonal designs in 24 and 28 runs," Working Papers 2015016, University of Antwerp, Faculty of Business and Economics.
    6. SCHOEN, Eric D. & VO-THANH, Nha & GOOS, Peter, 2016. "Orthogonal blocking arrangements for 24-run and 28-run two-level designs," Working Papers 2016002, University of Antwerp, Faculty of Business and Economics.

    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:compst:v:30:y:2015:i:4:p:1231-1244. 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.