Abstract:
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In this talk, we first consider the traditional Figures of Merit (FOMs), 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 FOMs are closely tied to simultaneous multivariate inference on an unknown parameter vector or unknown state vector. While it is useful to compute FOMs that involve averaging over all sources of randomness, when predicting an unknown (random) state, interest also lies in the predictive distribution. Analogous FOMS are developed from this distribution, and their usefulness is illustrated in a simulation experiment, where the goal is to determine the statistical properties associated with prediction of a multivariate atmospheric state from remotely sensed radiances.
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