IDEAS home Printed from https://ideas.repec.org/a/spr/jstada/v6y2019i1d10.1186_s40488-019-0092-4.html
   My bibliography  Save this article

Particle swarm based algorithms for finding locally and Bayesian D-optimal designs

Author

Listed:
  • Yu Shi

    (Department of Biostatistics, University of California at Los Angeles)

  • Zizhao Zhang

    (Department of Biostatistics, University of California at Los Angeles)

  • Weng Kee Wong

    (Department of Biostatistics, University of California at Los Angeles)

Abstract

When a model-based approach is appropriate, an optimal design can guide how to collect data judiciously for making reliable inference at minimal cost. However, finding optimal designs for a statistical model with several possibly interacting factors can be both theoretically and computationally challenging, and this issue is rarely discussed in the literature. We propose nature-inspired metaheuristic algorithms, like particle swarm optimization (PSO) and its variants, to solve such optimization problems. We demonstrate that such techniques, which are easy to implement, can find different types of optimal designs for models with several factors efficiently. To facilitate use of such algorithms, we provide computer codes to generate tailor made optimal designs and evaluate efficiencies of competing designs. As applications, we apply PSO and find Bayesian optimal designs for Exponential models useful in HIV studies and re-design a car-refuelling study for a Logistic model with ten factors and some interacting factors.

Suggested Citation

  • Yu Shi & Zizhao Zhang & Weng Kee Wong, 2019. "Particle swarm based algorithms for finding locally and Bayesian D-optimal designs," Journal of Statistical Distributions and Applications, Springer, vol. 6(1), pages 1-17, December.
    • Handle: RePEc:spr:jstada:v:6:y:2019:i:1:d:10.1186_s40488-019-0092-4
    DOI: 10.1186/s40488-019-0092-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40488-019-0092-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40488-019-0092-4?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
    ---><---

    References listed on IDEAS

    as
    1. Alan S. Perelson & Avidan U. Neumann & Martin Markowitz & John M. Leonard & David D. Ho, 1996. "HIV-1 Dynamics In Vivo: Virion Clearance Rate, Infected Cell Lifespan, and Viral Generation Time," Working Papers 96-02-004, Santa Fe Institute.
    2. Hulin Wu & A. Adam Ding, 1999. "Population HIV-1 Dynamics In Vivo: Applicable Models and Inferential Tools for Virological Data from AIDS Clinical Trials," Biometrics, The International Biometric Society, vol. 55(2), pages 410-418, 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. Stephen J. Walsh & John J. Borkowski, 2022. "Improved G -Optimal Designs for Small Exact Response Surface Scenarios: Fast and Efficient Generation via Particle Swarm Optimization," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    2. Wael Korani & Malek Mouhoub, 2021. "Review on Nature-Inspired Algorithms," SN Operations Research Forum, Springer, vol. 2(3), pages 1-26, 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. Marc Lavielle & Adeline Samson & Ana Karina Fermin & France Mentré, 2011. "Maximum Likelihood Estimation of Long-Term HIV Dynamic Models and Antiviral Response," Biometrics, The International Biometric Society, vol. 67(1), pages 250-259, March.
    2. A. Adam Ding & Hulin Wu, 2000. "A Comparison Study of Models and Fitting Procedures for Biphasic Viral Dynamics in HIV-1 Infected Patients Treated with Antiviral Therapies," Biometrics, The International Biometric Society, vol. 56(1), pages 293-300, March.
    3. Jianwei Chen, 2010. "Modelling long‐term human immunodeficiency virus dynamic models with application to acquired immune deficiency syndrome clinical study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 805-820, November.
    4. Tao Lu & Yangxin Huang & Min Wang & Feng Qian, 2014. "A refined parameter estimating approach for HIV dynamic model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1645-1657, August.
    5. González, Ramón E.R. & Coutinho, Sérgio & Zorzenon dos Santos, Rita Maria & de Figueirêdo, Pedro Hugo, 2013. "Dynamics of the HIV infection under antiretroviral therapy: A cellular automata approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4701-4716.
    6. Liu, Baisen & Wang, Liangliang & Nie, Yunlong & Cao, Jiguo, 2019. "Bayesian inference of mixed-effects ordinary differential equations models using heavy-tailed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 233-246.
    7. Hyejin KO & Marie Davidian, 2000. "Correcting for Measurement Error in Individual-Level Covariates in Nonlinear Mixed Effects Models," Biometrics, The International Biometric Society, vol. 56(2), pages 368-375, June.
    8. Sutimin, & Wijaya, Karunia Putra & Páez Chávez, Joseph & Tian, Tianhai, 2021. "An in-host HIV-1 infection model incorporating quiescent and activated CD4+ T cells as well as CTL response," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    9. Wei Liu & Shuyou Li, 2015. "A multiple imputation approach to nonlinear mixed-effects models with covariate measurement errors and missing values," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 463-476, March.
    10. Iraj Hosseini & Feilim Mac Gabhann, 2012. "Multi-Scale Modeling of HIV Infection in vitro and APOBEC3G-Based Anti-Retroviral Therapy," PLOS Computational Biology, Public Library of Science, vol. 8(2), pages 1-17, February.
    11. E Fabian Cardozo & Adriana Andrade & John W Mellors & Daniel R Kuritzkes & Alan S Perelson & Ruy M Ribeiro, 2017. "Treatment with integrase inhibitor suggests a new interpretation of HIV RNA decay curves that reveals a subset of cells with slow integration," PLOS Pathogens, Public Library of Science, vol. 13(7), pages 1-18, July.
    12. Nicola Bellomo & Richard Bingham & Mark A.J. Chaplain & Giovanni Dosi & Guido Forni & Damian A. Knopoff & John Lowengrub & Reidun Twarock & Maria Enrica Virgillito, 2020. "A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world," LEM Papers Series 2020/16, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. Sun, Hongquan & Li, Jin, 2020. "A numerical method for a diffusive virus model with general incidence function, cell-to-cell transmission and time delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    14. Lu, Xiaosun & Huang, Yangxin & Zhu, Yiliang, 2016. "Finite mixture of nonlinear mixed-effects joint models in the presence of missing and mismeasured covariate, with application to AIDS studies," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 119-130.
    15. A. M. Elaiw & N. H. AlShamrani & E. Dahy & A. A. Abdellatif & Aeshah A. Raezah, 2023. "Effect of Macrophages and Latent Reservoirs on the Dynamics of HTLV-I and HIV-1 Coinfection," Mathematics, MDPI, vol. 11(3), pages 1-26, January.
    16. Arthur, Joseph & Attarian, Adam & Hamilton, Franz & Tran, Hien, 2018. "Nonlinear Kalman filtering for censored observations," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 155-166.
    17. Hulin Wu & Hongqi Xue & Arun Kumar, 2012. "Numerical Discretization-Based Estimation Methods for Ordinary Differential Equation Models via Penalized Spline Smoothing with Applications in Biomedical Research," Biometrics, The International Biometric Society, vol. 68(2), pages 344-352, June.
    18. Dacheng Liu & Tao Lu & Xu-Feng Niu & Hulin Wu, 2011. "Mixed-Effects State-Space Models for Analysis of Longitudinal Dynamic Systems," Biometrics, The International Biometric Society, vol. 67(2), pages 476-485, June.
    19. Mojaver, Aida & Kheiri, Hossein, 2015. "Mathematical analysis of a class of HIV infection models of CD4+ T-cells with combined antiretroviral therapy," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 258-270.
    20. Chávez, Joseph Páez & Wijaya, Karunia Putra & Pinto, Carla M.A. & Burgos-Simón, Clara, 2022. "A model for type I diabetes in an HIV-infected patient under highly active antiretroviral therapy," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

    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:jstada:v:6:y:2019:i:1:d:10.1186_s40488-019-0092-4. 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.