Abstract
In this chapter we discuss the influence of a subset of observations on growth curve models from the Bayesian point of view. With a noninformative prior, the posterior distributions of parameters in growth curve models (GCMs) with Rao’s simple covariance structure SCS and unstructured covariance UC are obtained analytically, respectively. A Baysian entropy, namely, Kullback—Leibler divergence (KLD), as mentioned in Subsection 4.1.2 in Chapter 4, is used to measure the change of the posterior distributions when a subset of observations is deleted from the data. Also, the practical data studied in the pervious chapters are reanalyzed using the approaches addressed in this chapter.
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© 2002 Springer Science+Business Media New York
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Pan, JX., Fang, KT. (2002). Bayesian Influence Assessment. In: Growth Curve Models and Statistical Diagnostics. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21812-0_6
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DOI: https://doi.org/10.1007/978-0-387-21812-0_6
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2864-1
Online ISBN: 978-0-387-21812-0
eBook Packages: Springer Book Archive