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Innovations in Bayes and empirical Bayes methods: estimating parameters, populations and ranks by Thomas A. Louis; Wei Shen is a Mathematics article available to read on EtoBox.

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By formalizing the relation among components and &borrowing information' among them, Bayes and empirical Bayes methods can produce more valid, e$cient and informative statistical evaluations than those based on traditional methods. In addition, Bayesian structuring of complicated models and goals guides development of appropriate statistical approaches and generates summaries which properly account for sampling and modelling uncertainty. Computing innovations enable implementation of complex and relevant models, thereby substantially increasing the role of Bayes/empirical Bayes methods in important statistical assessments. Policy-relevant statistical assessments involve synthesis of information from a set of related components such as medical clinics, geographic regions or research studies. Typical assessments include inference for individual parameters, synthesis over the collection of components (for example, the parameter histogram) and comparisons among parameters (for example, ranks). The relative importance of these goals depends on the context. Bayesian structuring provides a guide to valid inference. For example, while posterior means are the &obvious' and optimal estimates

Who reads Innovations in Bayes and empirical Bayes methods: estimating parameters, populations and ranks?

Author

Thomas A. Louis; Wei Shen

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

1999

Language

EN

Field

Mathematics (Physical Sciences)