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Figures of merit for simultaneous inference and comparisons in simulation experiments
This article considers the traditional figures of merit, namely, bias and mean squared (prediction) error, which are typically used to evaluate simulation experiments. We propose functions of them that account for different variables' units; these alternative figures of merit are closely tied t...
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Published in: | Stat (International Statistical Institute) 2015, Vol.4 (1), p.196-211 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This article considers the traditional figures of merit, namely, bias and mean squared (prediction) error, which are typically used to evaluate simulation experiments. We propose functions of them that account for different variables' units; these alternative figures of merit are closely tied to simultaneous multivariate inference on an unknown parameter vector or unknown state vector. Their usefulness is illustrated in a simulation experiment, where the goal is to determine the statistical properties associated with prediction of a multivariate state. Copyright © 2015 John Wiley & Sons, Ltd. |
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ISSN: | 2049-1573 2049-1573 |
DOI: | 10.1002/sta4.88 |