Abstract
Research on multiple comparison ranking up to now has focused mainly on the comparison of preference in several objects (or items). This article presents a multiple comparison ranking procedure for comparing several scalar functions of parameters. A preference probability matrix for the procedure is introduced and its condition for warranting an optimal comparison ranking is studied. By using a Bayesian Monte Carlo method, we develop simulation-based procedure to estimate the matrix and obtain the optimal ranking via a row-sum scores method. The procedure is shown to be straightforward to specify distributionally and to implement computationally, with output readily adapted for required inference summaries. Necessary theory and illustrative examples are provided.
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Kim, HJ. A computational method for ranking L products of parameters. Computational Statistics 21, 473–485 (2006). https://doi.org/10.1007/s00180-006-0007-y
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DOI: https://doi.org/10.1007/s00180-006-0007-y