Estimative influence measures for the multivariate general linear model

Abstract Within the context of the multiviriate general linear model, and using a Bayesian formulation and Kullback-Leibler divergences this paper provides a framework and the resultant methods for the problem of detecting and characterizing influential subsets of observations when the goal is to estimate parameters. It is further indicated how these influence measures inherently depend upon one's exact estimative intent. The relationship to previous work on observations influential in estimation is discussed. The estimative influence measures obtained here are also compared with predictive influence functions previously obtained. Several examples are presented illustrating the methodology.

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