IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v97y2006i3p619-632.html
   My bibliography  Save this article

Restricted expected multivariate least squares

Author

Listed:
  • Fang, Kai-Tai
  • Wang, Song-Gui
  • von Rosen, Dietrich

Abstract

A new approach of estimating parameters in multivariate models is introduced. A fitting function will be used. The idea is to estimate parameters so that the fitting function equals or will be close to its expected value. The function will be decomposed into two parts. From one part, which will be independent of the mean parameters, the dispersion matrix is estimated. This estimator is inserted in the second part which then yields the estimators of the mean parameters. The Growth Curve model, extended Growth Curve model and a multivariate variance components model will illustrate the approach.

Suggested Citation

  • Fang, Kai-Tai & Wang, Song-Gui & von Rosen, Dietrich, 2006. "Restricted expected multivariate least squares," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 619-632, March.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:3:p:619-632
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(05)00048-5
    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. von Rosen, Dietrich, 1989. "Maximum likelihood estimators in multivariate linear normal models," Journal of Multivariate Analysis, Elsevier, vol. 31(2), pages 187-200, November.
    2. Dietrich Rosen, 1995. "Residuals in the growth curve model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(1), pages 129-136, January.
    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. Hamid, Jemila S. & Beyene, Joseph & von Rosen, Dietrich, 2011. "A novel trace test for the mean parameters in a multivariate growth curve model," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 238-251, February.
    2. Hamid Jemila S & Beyene Joseph, 2009. "A Multivariate Growth Curve Model for Ranking Genes in Replicated Time Course Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-28, July.
    3. Sayantee Jana & Narayanaswamy Balakrishnan & Jemila S. Hamid, 2020. "Inference in the Growth Curve Model under Multivariate Skew Normal Distribution," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 34-69, May.
    4. Pan, Jianxin, 2004. "Discordant outlier detection in the growth curve model with Rao's simple covariance structure," Statistics & Probability Letters, Elsevier, vol. 69(2), pages 135-142, August.
    5. Liu, Shuangzhe & Leiva, Víctor & Zhuang, Dan & Ma, Tiefeng & Figueroa-Zúñiga, Jorge I., 2022. "Matrix differential calculus with applications in the multivariate linear model and its diagnostics," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    6. Pan, Jian-Xin & Fang, Kai-Tai & von Rosen, Dietrich, 1998. "On the posterior distribution of the covariance matrix of the growth curve model," Statistics & Probability Letters, Elsevier, vol. 38(1), pages 33-39, May.
    7. Saulo, Helton & Vila, Roberto & Cordeiro, Shayane S. & Leiva, Víctor, 2023. "Bivariate symmetric Heckman models and their characterization," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    8. Yang, Guo-Qing & Wu, Qi-Guang, 2004. "Existence conditions for the uniformly minimum risk unbiased estimators in a class of linear models," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 76-88, January.
    9. Ohlson, Martin & von Rosen, Dietrich, 2010. "Explicit estimators of parameters in the Growth Curve model with linearly structured covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1284-1295, May.
    10. Hu, Jianhua & Liu, Fuxiang & Ahmed, S. Ejaz, 2012. "Estimation of parameters in the growth curve model via an outer product least squares approach for covariance," Journal of Multivariate Analysis, Elsevier, vol. 108(C), pages 53-66.
    11. Wu, Xiaoyong & Zou, Guohua & Chen, Jianwei, 2006. "Unbiased invariant minimum norm estimation in generalized growth curve model," Journal of Multivariate Analysis, Elsevier, vol. 97(8), pages 1718-1741, September.
    12. Bai, Peng & Shi, Lei, 2007. "Exact distributions of MLEs of regression coefficients in GMANOVA-MANOVA model," Journal of Multivariate Analysis, Elsevier, vol. 98(9), pages 1840-1852, October.
    13. Jian-Xin Pan & Kai-Tai Fang, 1996. "Influential observation in the growth curve model with unstructured covariance matrix," Computational Statistics & Data Analysis, Elsevier, vol. 22(1), pages 71-87, June.
    14. Li, Ying & Udén, Peter & von Rosen, Dietrich, 2015. "A two-step estimation method for grouped data with connections to the extended growth curve model and partial least squares regression," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 347-359.
    15. Jana, Sayantee & Balakrishnan, Narayanaswamy & Hamid, Jemila S., 2018. "Estimation of the parameters of the extended growth curve model under multivariate skew normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 111-128.
    16. Pierpaolo Angelini & Fabrizio Maturo, 2022. "The consumer’s demand functions defined to study contingent consumption plans," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1159-1175, June.
    17. Hu, Jianhua & Xin, Xin & You, Jinhong, 2014. "Model determination and estimation for the growth curve model via group SCAD penalty," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 199-213.
    18. Angelini, Pierpaolo & Maturo, Fabrizio, 2022. "The price of risk based on multilinear measures," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 39-57.
    19. Sayantee Jana & Narayanaswamy Balakrishnan & Dietrich Rosen & Jemila Seid Hamid, 2017. "High dimensional extension of the growth curve model and its application in genetics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(2), pages 273-292, June.

    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:97:y:2006:i:3:p:619-632. 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.