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Bayesian growth curve models with the generalized error distribution

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  • Zhiyong Zhang

Abstract

To deal with the longitudinal data with both leptokurtic and platykurtic errors, we extend growth curve models using the generalized error distribution (GED) model. The Metropolis--Hastings algorithm is used to estimate the GED model parameters in the Bayesian framework. The application of the GED model is illustrated through the analysis of mathematical development data. Results show that the GED model can correctly identify the deviation from normal of the error distributions.

Suggested Citation

  • Zhiyong Zhang, 2013. "Bayesian growth curve models with the generalized error distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1779-1795, August.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1779-1795
    DOI: 10.1080/02664763.2013.796348
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    References listed on IDEAS

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    1. Saralees Nadarajah, 2005. "A generalized normal distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 685-694.
    2. Ke-Hai Yuan & Peter Bentler & Wai Chan, 2004. "Structural equation modeling with heavy tailed distributions," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 421-436, September.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    5. William Meredith & John Tisak, 1990. "Latent curve analysis," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 107-122, March.
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    Cited by:

    1. Corinne Mulley & Liang Ma, 2018. "How the longer term success of a social marketing program is influenced by socio-demographics and the built environment," Transportation, Springer, vol. 45(2), pages 291-309, March.

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