StaLoP is a nonparametric dynamic panel prediction framework that weights historical panels via empirical discrepancy on target-local states, supplies bias-variance theory and simultaneous bands, and shows gains in simulations and applications.
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Similarity-Based Prediction for Digital Twins: Panel Data, Theory, and Applications
StaLoP is a nonparametric dynamic panel prediction framework that weights historical panels via empirical discrepancy on target-local states, supplies bias-variance theory and simultaneous bands, and shows gains in simulations and applications.