IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v83y2002i1p124-140.html
   My bibliography  Save this article

Distribution of Sum of Squares and Products Matrices for the Generalized Multilinear Matrix-T Model

Author

Listed:
  • Khan, Shahjahan

Abstract

The generalized multilinear model with the matrix-T error distribution is introduced in this paper. The sum of squares and products (SSP) matrix, as a counterpart of the Wishart matrix for the multinormal model, and the regression matrix for the errors and the observed as well as future responses are defined. The distributions of the regression matrix as well as the SSP matrix, and the prediction distribution of the future regression matrix and the future SSP matrix are derived.

Suggested Citation

  • Khan, Shahjahan, 2002. "Distribution of Sum of Squares and Products Matrices for the Generalized Multilinear Matrix-T Model," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 124-140, October.
    • Handle: RePEc:eee:jmvana:v:83:y:2002:i:1:p:124-140
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(01)92040-8
    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. Prucha, Ingmar R & Kelejian, Harry H, 1984. "The Structure of Simultaneous Equation Estimators: A Generalization towards Nonnormal Disturbances," Econometrica, Econometric Society, vol. 52(3), pages 721-736, 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. Bodnar, Taras & Mazur, Stepan & Okhrin, Yarema, 2013. "On the exact and approximate distributions of the product of a Wishart matrix with a normal vector," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 70-81.
    2. Kibria, B.M. Golam, 2006. "The matrix-t distribution and its applications in predictive inference," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 785-795, March.
    3. Liu, Jin Shan & Ip, Wai Cheung & Wong, Heung, 2009. "Predictive inference for singular multivariate elliptically contoured distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1440-1446, August.

    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. Hoek, Henk & Lucas, Andre & van Dijk, Herman K., 1995. "Classical and Bayesian aspects of robust unit root inference," Journal of Econometrics, Elsevier, vol. 69(1), pages 27-59, September.
    2. Ignacio Mauleon & Javier Perote, 2000. "Testing densities with financial data: an empirical comparison of the Edgeworth-Sargan density to the Student's t," The European Journal of Finance, Taylor & Francis Journals, vol. 6(2), pages 225-239.
    3. Akio Namba, 2001. "MSE performance of the 2SHI estimator in a regression model with multivariate t error terms," Statistical Papers, Springer, vol. 42(1), pages 81-96, January.
    4. Enzo D’Innocenzo & Alessandra Luati & Mario Mazzocchi, 2023. "A robust score-driven filter for multivariate time series," Econometric Reviews, Taylor & Francis Journals, vol. 42(5), pages 441-470, May.
    5. Mauleon, Ignacio, 2003. "Financial densities in emerging markets: an application of the multivariate ES density," Emerging Markets Review, Elsevier, vol. 4(2), pages 197-223, June.
    6. Sakata, Shinichi, 2007. "Instrumental variable estimation based on conditional median restriction," Journal of Econometrics, Elsevier, vol. 141(2), pages 350-382, December.
    7. Maronna, Ricardo A. & Yohai, Víctor J., 1994. "Robust estimation in simultaneous equations models," DES - Working Papers. Statistics and Econometrics. WS 3956, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Ignacio Mauleón, 2022. "Contributions to Risk Assessment with Edgeworth–Sargan Density Expansions (I): Stability Testing," Mathematics, MDPI, vol. 10(7), pages 1-18, March.
    9. Akio Namba & Kazuhiro Ohtani, 2007. "Risk comparison of the Stein-rule estimator in a linear regression model with omitted relevant regressors and multivariatet errors under the Pitman nearness criterion," Statistical Papers, Springer, vol. 48(1), pages 151-162, January.

    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:jmvana:v:83:y:2002:i:1:p:124-140. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.