IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v68y2012i1p212-214.html
   My bibliography  Save this article

Discussions

Author

Listed:
  • Heinz Schmidli
  • Beat Neuenschwander

Abstract

No abstract is available for this item.

Suggested Citation

  • Heinz Schmidli & Beat Neuenschwander, 2012. "Discussions," Biometrics, The International Biometric Society, vol. 68(1), pages 212-214, March.
  • Handle: RePEc:bla:biomet:v:68:y:2012:i:1:p:212-214
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01624.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. repec:dau:papers:123456789/1124 is not listed on IDEAS
    2. repec:dau:papers:123456789/4642 is not listed on IDEAS
    3. Sophie Donnet & Jean-Louis Foulley & Adeline Samson, 2010. "Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential Equations," Biometrics, The International Biometric Society, vol. 66(3), pages 733-741, September.
    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. Qianwen Tan & Subhashis Ghosal, 2021. "Bayesian Analysis of Mixed-effect Regression Models Driven by Ordinary Differential Equations," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 3-29, May.
    2. Delattre, Maud & Genon-Catalot, Valentine & Larédo, Catherine, 2018. "Parametric inference for discrete observations of diffusion processes with mixed effects," Stochastic Processes and their Applications, Elsevier, vol. 128(6), pages 1929-1957.
    3. Oscar García, 2019. "Estimating reducible stochastic differential equations by conversion to a least-squares problem," Computational Statistics, Springer, vol. 34(1), pages 23-46, March.
    4. Charlotte Dion, 2016. "Nonparametric estimation in a mixed-effect Ornstein–Uhlenbeck model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 919-951, November.
    5. Bukh, A.V. & Kashtanova, S.V. & Shepelev, I.A., 2023. "Complex error minimization algorithm with adaptive change rate," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    6. Libo Sun & Chihoon Lee & Jennifer A. Hoeting, 2019. "A penalized simulated maximum likelihood method to estimate parameters for SDEs with measurement error," Computational Statistics, Springer, vol. 34(2), pages 847-863, June.
    7. Palacios, Ana Paula & Marín Díazaraque, Juan Miguel & Quinto, Emiliano & Wiper, Michael Peter, 2012. "Bayesian modelling of bacterial growth for multiple populations," DES - Working Papers. Statistics and Econometrics. WS ws121610, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Wiqvist, Samuel & Golightly, Andrew & McLean, Ashleigh T. & Picchini, Umberto, 2021. "Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    9. Beskos, Alexandros & Kalogeropoulos, Konstantinos & Pazos, Erik, 2013. "Advanced MCMC methods for sampling on diffusion pathspace," Stochastic Processes and their Applications, Elsevier, vol. 123(4), pages 1415-1453.
    10. Sun, Libo & Lee, Chihoon & Hoeting, Jennifer A., 2015. "A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 54-67.
    11. Zhong, Guang-Yan & He, Feng & Li, Jiang-Cheng & Mei, Dong-Cheng & Tang, Nian-Sheng, 2019. "Coherence resonance-like and efficiency of financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    12. Picchini, Umberto & Anderson, Rachele, 2017. "Approximate maximum likelihood estimation using data-cloning ABC," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 166-183.
    13. Picchini, Umberto & Ditlevsen, Susanne, 2011. "Practical estimation of high dimensional stochastic differential mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1426-1444, March.

    More about this item

    Statistics

    Access and download statistics

    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:bla:biomet:v:68:y:2012:i:1:p:212-214. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.