Abstract
From a Bayesian point of view, in this paper we discuss the influence of a subset of observations on the posterior distributions of parameters in a growth curve model with unstructured covariance. The measure used to assess the influence is based on a Bayesian entropy, namely Kullback-Leibler divergence (KLD). Several new properties of the Bayesian entropy are studied, and analytically closed forms of the KLD measurement both for the matrix-variate normal distribution and the Wishart distribution are established. In the growth curve model, the KLD measurements for all combinations of the parameters are also studied. For illustration, a practical data set is analyzed using the proposed approach, which shows that the diagnostics measurements are useful in practice.
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Pan, JX., Fung, WK. Bayesian Influence Assessment in the Growth Curve Model with Unstructured Covariance. Annals of the Institute of Statistical Mathematics 52, 737–752 (2000). https://doi.org/10.1023/A:1017581411504
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DOI: https://doi.org/10.1023/A:1017581411504