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


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

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 58 (1996)
    Issue (Month): 1 (July)
    Pages: 55-81

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    Handle: RePEc:eee:jmvana:v:58:y:1996:i:1:p:55-81

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    Keywords: Bayesian Hessian matrix Bayesian local influence covariance-weighted perturbation growth curve model Kullback-Leibler divergence statistical diagnostics;


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    Cited by:
    1. 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.
    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. 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.
    4. Sik-Yum Lee & Nian-Sheng Tang, 2004. "Local influence analysis of nonlinear structural equation models," Psychometrika, Springer, vol. 69(4), pages 573-592, December.


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