IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i2p272-289.html
   My bibliography  Save this article

Efficient Monte Carlo computation of Fisher information matrix using prior information

Author

Listed:
  • Das, Sonjoy
  • Spall, James C.
  • Ghanem, Roger

Abstract

The Fisher information matrix (FIM) is a critical quantity in several aspects of mathematical modeling, including input selection and confidence region calculation. Analytical determination of the FIM in a general setting, especially in nonlinear models, may be difficult or almost impossible due to intractable modeling requirements or/and intractable high-dimensional integration. To circumvent these difficulties, a Monte Carlo simulation based technique, known as resampling algorithm, is usually recommended, in which values of the log-likelihood function or its exact stochastic gradient computed based on a set of pseudo-data vectors are used. The current work proposes an extension of this resampling algorithm in order to enhance the statistical qualities of the estimator of the FIM. This modified resampling algorithm is useful in those cases when some elements of the FIM are analytically known from prior information and the rest of the elements are unknown. The estimator of the FIM resulting from the proposed algorithm simultaneously preserves the analytically known elements and reduces variances of the estimators of the unknown elements. This is achieved by capitalizing on the information contained in the known elements.

Suggested Citation

  • Das, Sonjoy & Spall, James C. & Ghanem, Roger, 2010. "Efficient Monte Carlo computation of Fisher information matrix using prior information," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 272-289, February.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:2:p:272-289
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00344-2
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Neerchal, Nagaraj K. & Morel, Jorge G., 2005. "An improved method for the computation of maximum likeliood estimates for multinomial overdispersion models," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 33-43, April.
    2. Nadarajah, Saralees, 2009. "An alternative inverse Gaussian distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1721-1729.
    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. Boubacar Mainassara, Y. & Carbon, M. & Francq, C., 2012. "Computing and estimating information matrices of weak ARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 345-361.

    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. Corsini, Noemi & Viroli, Cinzia, 2022. "Dealing with overdispersion in multivariate count data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    2. Samanta, Suvajit & Li, Yi-Ju & Weir, Bruce S., 2009. "Drawing inferences about the coancestry coefficient," Theoretical Population Biology, Elsevier, vol. 75(4), pages 312-319.
    3. Franck Adékambi & Essodina Takouda, 2022. "On the Discounted Penalty Function in a Perturbed Erlang Renewal Risk Model With Dependence," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 481-513, June.
    4. Hernández-Veleros, Zeus Salvador, 2010. "Heterogeneous growth cycles/Ciclos de crecimiento heterogéneo," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 28, pages 625-650, Diciembre.
    5. Tvedebrink, Torben, 2010. "Overdispersion in allelic counts and θ-correction in forensic genetics," Theoretical Population Biology, Elsevier, vol. 78(3), pages 200-210.
    6. Andrew M. Raim & Nagaraj K. Neerchal & Jorge G. Morel, 2017. "An approximation to the information matrix of exponential family finite mixtures," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(2), pages 333-364, April.

    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:eee:csdana:v:54:y:2010:i:2:p:272-289. 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/locate/csda .

    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.