IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v58y1996i1p55-81.html
   My bibliography  Save this article

Bayesian Local Influence for the Growth Curve Model with Rao's Simple Covariance Structure

Author

Listed:
  • Pan, Jian-Xin
  • Fang, Kai-Tai
  • Liski, Erkki P.

Abstract

In this paper, the Bayesian local influence approach is employed to diagnose the adequacy of the growth curve model with Rao's simple covariance structure, based on the Kullback-Leibler divergence. The Bayesian Hessian matrices of the model are investigated in detail under an abstract perturbation scheme. For illustration, covariance-weighted perturbation is considered particularly and used to analyze two real-life biological data sets, which shows that the criteria presented in this article are useful in practice.

Suggested Citation

  • Pan, Jian-Xin & Fang, Kai-Tai & Liski, Erkki P., 1996. "Bayesian Local Influence for the Growth Curve Model with Rao's Simple Covariance Structure," Journal of Multivariate Analysis, Elsevier, vol. 58(1), pages 55-81, July.
  • Handle: RePEc:eee:jmvana:v:58:y:1996:i:1:p:55-81
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(96)90039-1
    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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jian-Xin Pan & Wing-Kam Fung, 2000. "Bayesian Influence Assessment in the Growth Curve Model with Unstructured Covariance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(4), pages 737-752, December.
    2. Lee, Sik-Yum & Lu, Bin & Song, Xin-Yuan, 2006. "Assessing local influence for nonlinear structural equation models with ignorable missing data," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1356-1377, March.
    3. Xiaowen Dai & Libin Jin & Maozai Tian & Lei Shi, 2019. "Bayesian Local Influence for Spatial Autoregressive Models with Heteroscedasticity," Statistical Papers, Springer, vol. 60(5), pages 1423-1446, October.
    4. Sik-Yum Lee & Nian-Sheng Tang, 2004. "Local influence analysis of nonlinear structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 69(4), pages 573-592, December.
    5. 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.
    6. Gu, Hong & Fung, Wing K., 2001. "Influence Diagnostics in Common Principal Components Analysis," Journal of Multivariate Analysis, Elsevier, vol. 79(2), pages 275-294, November.

    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:58:y:1996:i:1:p:55-81. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.