IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2007i2p869-878.html

Optimal multilinear estimation of a random vector under constraints of causality and limited memory

Author

Listed:
  • Howlett, P.G.
  • Torokhti, A.
  • Pearce, C.E.M.

Abstract

No abstract is available for this item.

Suggested Citation

  • Howlett, P.G. & Torokhti, A. & Pearce, C.E.M., 2007. "Optimal multilinear estimation of a random vector under constraints of causality and limited memory," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 869-878, October.
  • Handle: RePEc:eee:csdana:v:52:y:2007:i:2:p:869-878
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(06)00388-4
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Anatoli Torokhti & Phil Howlett & Charles Pearce, 2003. "Optimal Mathematical Models for Nonlinear Dynamical Systems," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 9(3), pages 327-343, September.
    2. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    3. Kauermann G. & Carroll R.J., 2001. "A Note on the Efficiency of Sandwich Covariance Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1387-1396, December.
    4. Torokhti, Anatoli & Howlett, Phil, 2003. "Constructing fixed rank optimal estimators with method of best recurrent approximations," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 293-309, August.
    5. Kubokawa, T. & Srivastava, M. S., 2003. "Estimating the covariance matrix: a new approach," Journal of Multivariate Analysis, Elsevier, vol. 86(1), pages 28-47, July.
    6. Lihong Wang, 2004. "Asymptotics of estimates in constrained nonlinear regression with long-range dependent innovations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 56(2), pages 251-264, June.
    7. Champion, Colin J., 2003. "Empirical Bayesian estimation of normal variances and covariances," Journal of Multivariate Analysis, Elsevier, vol. 87(1), pages 60-79, October.
    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. Hannart, Alexis & Naveau, Philippe, 2014. "Estimating high dimensional covariance matrices: A new look at the Gaussian conjugate framework," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 149-162.
    2. Weilong Liu & Yanchu Liu, 2025. "Covariance Matrix Estimation for Positively Correlated Assets," Papers 2507.01545, arXiv.org.
    3. Nathan Lassance & Alberto Martín-Utrera & Majeed Simaan, 2024. "The Risk of Expected Utility Under Parameter Uncertainty," Management Science, INFORMS, vol. 70(11), pages 7644-7663, November.
    4. Bagnara, Matteo & Vaucher, Benoit, 2025. "Risk diversification and extreme risk mitigation," Journal of Empirical Finance, Elsevier, vol. 83(C).
    5. Konno, Yoshihiko, 2009. "Shrinkage estimators for large covariance matrices in multivariate real and complex normal distributions under an invariant quadratic loss," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2237-2253, November.
    6. Wessel N. Wieringen & Gwenaël G. R. Leday, 2024. "Ridge-type covariance and precision matrix estimators of the multivariate normal distribution," Statistical Papers, Springer, vol. 65(9), pages 5835-5849, December.
    7. Yuan, Ke-Hai & Chan, Wai, 2008. "Structural equation modeling with near singular covariance matrices," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4842-4858, June.
    8. Davit Gondauri, 2026. "P vs NP Problem in Portfolio Optimization: Integrating the Markowitz-CAPM Framework with Cardinality Constraints and Black-Scholes Derivative Pricing," Papers 2603.15652, arXiv.org.
    9. Christian Bongiorno, 2020. "Bootstraps Regularize Singular Correlation Matrices," Working Papers hal-02536278, HAL.
    10. Ali Atiah Alzahrani, 2025. "Multi-Agent Regime-Conditioned Diffusion (MARCD) for CVaR-Constrained Portfolio Decisions," Papers 2510.10807, arXiv.org, revised Nov 2025.
    11. Arbia, Giuseppe & Bramante, Riccardo & Facchinetti, Silvia & Zappa, Diego, 2018. "Modeling inter-country spatial financial interactions with Graphical Lasso: An application to sovereign co-risk evaluation," Regional Science and Urban Economics, Elsevier, vol. 70(C), pages 72-79.
    12. Tae-Hwy Lee & Ekaterina Seregina, 2024. "Optimal Portfolio Using Factor Graphical Lasso," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 670-695.
    13. Ding, Hui & Zhang, Jian & Zhang, Riquan, 2022. "Nonparametric variable screening for multivariate additive models," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    14. van Wieringen, Wessel N. & Stam, Koen A. & Peeters, Carel F.W. & van de Wiel, Mark A., 2020. "Updating of the Gaussian graphical model through targeted penalized estimation," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    15. Huyen Pham & Xiaoli Wei & Chao Zhou, 2018. "Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach," Papers 1809.01464, arXiv.org, revised Dec 2021.
    16. Soudeep Deb & Rishideep Roy & Shubhabrata Das, 2024. "Forecasting elections from partial information using a Bayesian model for a multinomial sequence of data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1814-1834, September.
    17. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combination," Working Papers 202024, University of California at Riverside, Department of Economics.
    18. Yiyao Zhang & Diksha Goel & Hussain Ahmad & Claudia Szabo, 2025. "RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets," Papers 2510.14986, arXiv.org.
    19. Steland, Ansgar, 2020. "Testing and estimating change-points in the covariance matrix of a high-dimensional time series," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
    20. Ben R. Craig & Margherita Giuzio & Sandra Paterlini, 2019. "The Effect of Possible EU Diversification Requirements on the Risk of Banks’ Sovereign Bond Portfolios," Working Papers 19-12, Federal Reserve Bank of Cleveland.

    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:52:y:2007:i:2:p:869-878. 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.