IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v22y2013i1p159-181.html
   My bibliography  Save this article

Optimal designs for some stochastic processes whose covariance is a function of the mean

Author

Listed:
  • Mariano Amo-Salas
  • Jesús López-Fidalgo
  • Emilio Porcu

Abstract

This paper considers optimal experimental designs for models with correlated observations through a covariance function depending on the magnitude of the responses. This suggests the use of stochastic processes whose covariance structure is a function of the mean. Covariance functions must be positive definite. This fact is nontrivial in this context and constitutes one of the challenges of the present paper. We show that there exists a huge class of functions that, composed with the mean of the process in some way, preserves positive definiteness and can be used for the purposes of modeling and computing optimal designs in more realistic situations. We offer some examples for an easy construction of such covariances and then study the problem of locally D-optimal designs through an illustrative example as well as a real radiation retention model in the human body. Copyright Sociedad de Estadística e Investigación Operativa 2013

Suggested Citation

  • Mariano Amo-Salas & Jesús López-Fidalgo & Emilio Porcu, 2013. "Optimal designs for some stochastic processes whose covariance is a function of the mean," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 159-181, March.
  • Handle: RePEc:spr:testjl:v:22:y:2013:i:1:p:159-181
    DOI: 10.1007/s11749-012-0311-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11749-012-0311-5
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11749-012-0311-5?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. J. López-Fidalgo & R. Martín-Martín & M. Stehlík, 2008. "Marginally restricted D-optimal designs for correlated observations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(6), pages 617-632.
    2. Angelis, L. & Bora-Senta, E. & Moyssiadis, C., 2001. "Optimal exact experimental designs with correlated errors through a simulated annealing algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 37(3), pages 275-296, September.
    3. Holger Dette & Weng Kee Wong, 1999. "Optimal Designs When the Variance Is A Function of the Mean," Biometrics, The International Biometric Society, vol. 55(3), pages 925-929, September.
    4. Ucinski Dariusz & Atkinson Anthony C., 2004. "Experimental Design for Time-Dependent Models with Correlated Observations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-16, May.
    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. Santiago Campos-Barreiro & Jesús López-Fidalgo, 2015. "D-optimal experimental designs for a growth model applied to a Holstein-Friesian dairy farm," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 491-505, September.
    2. Víctor Casero-Alonso & Jesús López-Fidalgo, 2015. "Optimal designs subject to cost constraints in simultaneous equations models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 701-713, 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. Patan, Maciej & Bogacka, Barbara, 2007. "Optimum experimental designs for dynamic systems in the presence of correlated errors," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5644-5661, August.
    2. Hertel, Ida & Kohler, Michael, 2013. "Estimation of the optimal design of a nonlinear parametric regression problem via Monte Carlo experiments," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 1-12.
    3. Santiago Campos-Barreiro & Jesús López-Fidalgo, 2015. "D-optimal experimental designs for a growth model applied to a Holstein-Friesian dairy farm," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 491-505, September.
    4. Kai Yzenbrandt & Julie Zhou, 2022. "Minimax robust designs for regression models with heteroscedastic errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 203-222, February.
    5. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
    6. Dette, Holger & Pepelyshev, Andrey & Zhigljavsky, Anatoly, 2014. "‘Nearly’ universally optimal designs for models with correlated observations," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1103-1112.
    7. Payne, Roger W., 2003. "General balance, large data sets and extensions to unbalanced treatment structures," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 297-304, October.
    8. Haoyu Wang & Chongqi Zhang, 2022. "The mixture design threshold accepting algorithm for generating $$\varvec{D}$$ D -optimal designs of the mixture models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(3), pages 345-371, April.
    9. Ioannidis Evangelos & Merkouris Takis & Zhang Li-Chun & Karlberg Martin & Petrakos Michalis & Reis Fernando & Stavropoulos Photis, 2016. "On a Modular Approach to the Design of Integrated Social Surveys," Journal of Official Statistics, Sciendo, vol. 32(2), pages 259-286, June.
    10. Maria P. Braun & Simos G. Meintanis & Viatcheslav B. Melas, 2008. "Optimal Design Approach to GMM Estimation of Parameters Based on Empirical Transforms," International Statistical Review, International Statistical Institute, vol. 76(3), pages 387-400, December.
    11. Łukasz Smaga, 2016. "A note on D-optimal chemical balance weighing designs with autocorrelated observations," Statistical Papers, Springer, vol. 57(3), pages 721-730, September.
    12. Singh, Rakhi & Mukhopadhyay, Siuli, 2019. "Exact Bayesian designs for count time series," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 157-170.

    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:testjl:v:22:y:2013:i:1:p:159-181. 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.