Innovations in bayes and empirical bayes methods: estimating parameters, populations and ranks.
By formalizing the relation among components and 'borrowing information' among them, Bayes and empirical Bayes methods can produce more valid, efficient 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 for individual components under squared error loss, their empirical distribution function (EDF) is underdispersed and never valid for estimating the EDF of the true, underlying parameters. Effective histogram estimates result from optimizing a loss function based in a distance between the histogram and its estimate. Similarly, ranking observed data usually produces poor estimates and ranking posterior means can be inappropriate. Effective estimates should be based on a loss function that caters directly to ranks. Using examples of 'borrowing information', shrinkage and the variance/bias trade-off we motivate Bayes and empirical Bayes analysis. Then, we outline the formal approach and discuss 'triple-goal' estimates with values that when ranked produce optimal ranks, for which the EDF is an optimal estimate of the parameter EDF and such that the values themselves are effective estimates of co-ordinate-specific parameters. We use basic models and data analysis examples to highlight the conceptual and structural issues.
Louis
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Division of Biostatistics, The University of Minnesota, School of Public Health, 420 Delaware Street, Box 303, Minneapolis, MN 55455, USA. tom@biostat.umn.edu
Shen
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D000818:Animals; D001499:Bayes Theorem; D001681:Biological Assay; D002273:Carcinogens / Q000633:toxicity; D001026:Coronary Artery Bypass / Q000401:mortality; D003627:Data Interpretation, Statistical; D006166:Guatemala / Q000453:epidemiology; D006721:Honduras / Q000453:epidemiology; D006801:Humans; D016013:Likelihood Functions; D016011:Normal Distribution; D012377:Rodentia; D014123:Toxoplasmosis / Q000453:epidemiology
D002273:Carcinogens